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Advanced Internet protocols, services, and applications [[electronic resource] /] / Eiji Oki ... [et al.]
| Advanced Internet protocols, services, and applications [[electronic resource] /] / Eiji Oki ... [et al.] |
| Autore | Oki Eiji <1969-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N. J., : John Wiley & Sons, Inc., c2012 |
| Descrizione fisica | 1 online resource (262 p.) |
| Disciplina | 004.6 |
| Altri autori (Persone) |
Rojas-CessaRoberto
TatipamulaMallikarjun VogtChristian (Marketing executive) |
| Soggetto topico |
Computer network protocols
Computer networks |
| ISBN |
1-280-59135-8
9786613621184 1-118-18081-X 1-118-18082-8 1-118-18080-1 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Internet Protocols, Services, and Applications; CONTENTS; Preface; Acknowledgments; About the Authors; 1 Transmission Control Protocol/Internet Protocol Overview; 1.1 Fundamental Architecture; 1.2 Internet Protocol Basics; 1.2.1 Packet Header; 1.2.2 Internet Protocol Address; 1.2.3 Internet Protocol Classification; 1.2.4 Subnet and its Masking; 1.2.5 Subnet Calculation; 1.3 Routing; 1.3.1 Routing across Providers; 1.3.2 Routing within Edge Networks; 1.3.3 Routing Scalability; References; 2 Transport-Layer Protocols; 2.1 Transmission Control Protocol
2.1.1 Transmission Control Protocol Header Structure2.1.2 Three-Way Handshake; 2.1.3 Transmission Control Protocol Flow Control and Congestion Control; 2.1.4 Port Number; 2.2 User Datagram Protocol; 2.2.1 User Datagram Protocol Header Structure; 2.3 Stream Control Transmission Protocol; 2.3.1 Stream Control Transmission Protocol Packet Structure; 2.3.2 Security: Prevention of SYN Attacks; 2.4 Real-Time Transport Protocol; 2.4.1 Real-Time Transport Protocol Header Structure; References; 3 Internet Architecture; 3.1 Internet Exchange Point; 3.2 History of Internet Exchange Points 3.3 Internet Service Provider Interconnection Relationships3.4 Peering and Transit; References; 4 IP Routing Protocols; 4.1 Overview of Routing Protocols; 4.1.1 Interior Gateway Protocol; 4.1.2 Exterior Gateway Protocol; 4.2 Routing Information Protocol; 4.2.1 Routing Information Protocol Header Format; 4.2.2 Update of Routing Table in Routing Information Protocol; 4.2.3 Maintenance of Routing Table in Routing Information Protocol; 4.2.4 Split Horizon; 4.2.5 Limitations of Routing Information Protocol; 4.3 Open Shortest Path First; 4.3.1 Shortest-Path Algorithm; 4.3.2 Hierarchical Routing 4.3.3 Open Shortest Path First Packet Format4.3.4 Comparison of Routing Information Protocol and Open Shortest Path First; 4.4 Border Gateway Protocol; 4.4.1 Border Gateway Protocol Message Flows; 4.4.2 Border Gateway Protocol Policy Selection Attributes; References; 5 Multiprotocol Label Switching; 5.1 Overview; 5.2 Functions and Mechanisms; 5.3 Applicabilities; References; 6 IP Quality Of Service; 6.1 Introduction; 6.2 Quality of Service in IP Version 4; 6.3 Integrated Services; 6.3.1 Packet Scheduler; 6.3.2 Packet Classifier; 6.3.3 Admission Control 6.3.4 Resource Reservation Protocol (RSVP)6.4 Differentiated Services; 6.5 Quality Of Service with Nested Differentiated Services Levels; 6.5.1 Drawbacks of Explicit Endpoint Admission Control with Path Selection; 6.5.2 OSPF-Based Adaptive and Flexible Quality of Service Provisioning; 6.5.3 Combination of Security and Quality of Service; 6.5.4 Path Selection Algorithm Analysis; References; 7 IP Multicast and Anycast; 7.1 Addressing; 7.1.1 Multicast Addressing; 7.1.2 Differences between Multicasting and Multiple Unicasting; 7.2 Multicast Routing; 7.2.1 Optimal Routing: Shortest-Path Trees 7.2.2 Unicast Routing |
| Record Nr. | UNINA-9910141339103321 |
Oki Eiji <1969->
|
||
| Hoboken, N. J., : John Wiley & Sons, Inc., c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advanced Internet protocols, services, and applications [[electronic resource] /] / Eiji Oki ... [et al.]
| Advanced Internet protocols, services, and applications [[electronic resource] /] / Eiji Oki ... [et al.] |
| Autore | Oki Eiji <1969-> |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N. J., : John Wiley & Sons, Inc., c2012 |
| Descrizione fisica | 1 online resource (262 p.) |
| Disciplina | 004.6 |
| Altri autori (Persone) |
Rojas-CessaRoberto
TatipamulaMallikarjun VogtChristian (Marketing executive) |
| Soggetto topico |
Computer network protocols
Computer networks |
| ISBN |
1-280-59135-8
9786613621184 1-118-18081-X 1-118-18082-8 1-118-18080-1 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Advanced Internet Protocols, Services, and Applications; CONTENTS; Preface; Acknowledgments; About the Authors; 1 Transmission Control Protocol/Internet Protocol Overview; 1.1 Fundamental Architecture; 1.2 Internet Protocol Basics; 1.2.1 Packet Header; 1.2.2 Internet Protocol Address; 1.2.3 Internet Protocol Classification; 1.2.4 Subnet and its Masking; 1.2.5 Subnet Calculation; 1.3 Routing; 1.3.1 Routing across Providers; 1.3.2 Routing within Edge Networks; 1.3.3 Routing Scalability; References; 2 Transport-Layer Protocols; 2.1 Transmission Control Protocol
2.1.1 Transmission Control Protocol Header Structure2.1.2 Three-Way Handshake; 2.1.3 Transmission Control Protocol Flow Control and Congestion Control; 2.1.4 Port Number; 2.2 User Datagram Protocol; 2.2.1 User Datagram Protocol Header Structure; 2.3 Stream Control Transmission Protocol; 2.3.1 Stream Control Transmission Protocol Packet Structure; 2.3.2 Security: Prevention of SYN Attacks; 2.4 Real-Time Transport Protocol; 2.4.1 Real-Time Transport Protocol Header Structure; References; 3 Internet Architecture; 3.1 Internet Exchange Point; 3.2 History of Internet Exchange Points 3.3 Internet Service Provider Interconnection Relationships3.4 Peering and Transit; References; 4 IP Routing Protocols; 4.1 Overview of Routing Protocols; 4.1.1 Interior Gateway Protocol; 4.1.2 Exterior Gateway Protocol; 4.2 Routing Information Protocol; 4.2.1 Routing Information Protocol Header Format; 4.2.2 Update of Routing Table in Routing Information Protocol; 4.2.3 Maintenance of Routing Table in Routing Information Protocol; 4.2.4 Split Horizon; 4.2.5 Limitations of Routing Information Protocol; 4.3 Open Shortest Path First; 4.3.1 Shortest-Path Algorithm; 4.3.2 Hierarchical Routing 4.3.3 Open Shortest Path First Packet Format4.3.4 Comparison of Routing Information Protocol and Open Shortest Path First; 4.4 Border Gateway Protocol; 4.4.1 Border Gateway Protocol Message Flows; 4.4.2 Border Gateway Protocol Policy Selection Attributes; References; 5 Multiprotocol Label Switching; 5.1 Overview; 5.2 Functions and Mechanisms; 5.3 Applicabilities; References; 6 IP Quality Of Service; 6.1 Introduction; 6.2 Quality of Service in IP Version 4; 6.3 Integrated Services; 6.3.1 Packet Scheduler; 6.3.2 Packet Classifier; 6.3.3 Admission Control 6.3.4 Resource Reservation Protocol (RSVP)6.4 Differentiated Services; 6.5 Quality Of Service with Nested Differentiated Services Levels; 6.5.1 Drawbacks of Explicit Endpoint Admission Control with Path Selection; 6.5.2 OSPF-Based Adaptive and Flexible Quality of Service Provisioning; 6.5.3 Combination of Security and Quality of Service; 6.5.4 Path Selection Algorithm Analysis; References; 7 IP Multicast and Anycast; 7.1 Addressing; 7.1.1 Multicast Addressing; 7.1.2 Differences between Multicasting and Multiple Unicasting; 7.2 Multicast Routing; 7.2.1 Optimal Routing: Shortest-Path Trees 7.2.2 Unicast Routing |
| Record Nr. | UNINA-9910823090003321 |
|
Oki Eiji <1969->
|
||
| Hoboken, N. J., : John Wiley & Sons, Inc., c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali
| Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali |
| Autore | Gebali Fayez |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (365 p.) |
| Disciplina |
004.35
004/.35 005.275 |
| Collana | Wiley series on parallel and distributed computing |
| Soggetto topico |
Parallel processing (Electronic computers)
Computer algorithms |
| ISBN |
1-283-02557-4
9786613025579 0-470-93201-5 0-470-93202-3 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Algorithms and Parallel Computing; Contents; Preface; List of Acronyms; Chapter 1: Introduction; 1.1 INTRODUCTION; 1.2 TOWARD AUTOMATING PARALLEL PROGRAMMING; 1.3 ALGORITHMS; 1.4 PARALLEL COMPUTING DESIGN CONSIDERATIONS; 1.5 PARALLEL ALGORITHMS AND PARALLEL ARCHITECTURES; 1.6 RELATING PARALLEL ALGORITHM AND PARALLEL ARCHITECTURE; 1.7 IMPLEMENTATION OF ALGORITHMS: A TWO-SIDED PROBLEM; 1.8 MEASURING BENEFITS OF PARALLEL COMPUTING; 1.9 AMDAHL'S LAW FOR MULTIPROCESSOR SYSTEMS; 1.10 GUSTAFSON-BARSIS'S LAW; 1.11 APPLICATIONS OF PARALLEL COMPUTING; Chapter 2: Enhancing Uniprocessor Performance
2.1 INTRODUCTION2.2 INCREASING PROCESSOR CLOCK FREQUENCY; 2.3 PARALLELIZING ALU STRUCTURE; 2.4 USING MEMORY HIERARCHY; 2.5 PIPELINING; 2.6 VERY LONG INSTRUCTION WORD (VLIW) PROCESSORS; 2.7 INSTRUCTION-LEVEL PARALLELISM (ILP) AND SUPERSCALAR PROCESSORS; 2.8 MULTITHREADED PROCESSOR; Chapter 3: Parallel Computers; 3.1 INTRODUCTION; 3.2 PARALLEL COMPUTING; 3.3 SHARED-MEMORY MULTIPROCESSORS (UNIFORM MEMORY ACCESS [UMA]); 3.4 DISTRIBUTED-MEMORY MULTIPROCESSOR (NONUNIFORM MEMORY ACCESS [NUMA]); 3.5 SIMD PROCESSORS; 3.6 SYSTOLIC PROCESSORS; 3.7 CLUSTER COMPUTING; 3.8 GRID (CLOUD) COMPUTING 3.9 MULTICORE SYSTEMS3.10 SM; 3.11 COMMUNICATION BETWEEN PARALLEL PROCESSORS; 3.12 SUMMARY OF PARALLEL ARCHITECTURES; Chapter 4: Shared-Memory Multiprocessors; 4.1 INTRODUCTION; 4.2 CACHE COHERENCE AND MEMORY CONSISTENCY; 4.3 SYNCHRONIZATION AND MUTUAL EXCLUSION; Chapter 5: Interconnection Networks; 5.1 INTRODUCTION; 5.2 CLASSIFICATION OF INTERCONNECTION NETWORKS BY LOGICAL TOPOLOGIES; 5.3 INTERCONNECTION NETWORK SWITCH ARCHITECTURE; Chapter 6: Concurrency Platforms; 6.1 INTRODUCTION; 6.2 CONCURRENCY PLATFORMS; 6.3 CILK++; 6.4 OpenMP; 6.5 COMPUTE UNIFIED DEVICE ARCHITECTURE (CUDA) Chapter 7: Ad Hoc Techniques for Parallel Algorithms7.1 INTRODUCTION; 7.2 DEFINING ALGORITHM VARIABLES; 7.3 INDEPENDENT LOOP SCHEDULING; 7.4 DEPENDENT LOOPS; 7.5 LOOP SPREADING FOR SIMPLE DEPENDENT LOOPS; 7.6 LOOP UNROLLING; 7.7 PROBLEM PARTITIONING; 7.8 DIVIDE-AND-CONQUER (RECURSIVE PARTITIONING) STRATEGIES; 7.9 PIPELINING; Chapter 8: Nonserial-Parallel Algorithms; 8.1 INTRODUCTION; 8.2 COMPARING DAG AND DCG ALGORITHMS; 8.3 PARALLELIZING NSPA ALGORITHMS REPRESENTED BY A DAG; 8.4 FORMAL TECHNIQUE FOR ANALYZING NSPAs; 8.5 DETECTING CYCLES IN THE ALGORITHM 8.6 EXTRACTING SERIAL AND PARALLEL ALGORITHM PERFORMANCE PARAMETERS8.7 USEFUL THEOREMS; 8.8 PERFORMANCE OF SERIAL AND PARALLEL ALGORITHMS ON PARALLEL COMPUTERS; Chapter 9: z-Transform Analysis; 9.1 INTRODUCTION; 9.2 DEFINITION OF z-TRANSFORM; 9.3 THE 1-D FIR DIGITAL FILTER ALGORITHM; 9.4 SOFTWARE AND HARDWARE IMPLEMENTATIONS OF THE z-TRANSFORM; 9.5 DESIGN 1: USING HORNER'S RULE FOR BROADCAST INPUT AND PIPELINED OUTPUT; 9.6 DESIGN 2: PIPELINED INPUT AND BROADCAST OUTPUT; 9.7 DESIGN 3: PIPELINED INPUT AND OUTPUT; Chapter 10: Dependence Graph Analysis; 10.1 INTRODUCTION 10.2 THE 1-D FIR DIGITAL FILTER ALGORITHM |
| Record Nr. | UNINA-9910133581903321 |
|
Gebali Fayez
|
||
| Hoboken, N.J., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali
| Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali |
| Autore | Gebali Fayez |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (365 p.) |
| Disciplina |
004.35
004/.35 005.275 |
| Collana | Wiley series on parallel and distributed computing |
| Soggetto topico |
Parallel processing (Electronic computers)
Computer algorithms |
| ISBN |
1-283-02557-4
9786613025579 0-470-93201-5 0-470-93202-3 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Algorithms and Parallel Computing; Contents; Preface; List of Acronyms; Chapter 1: Introduction; 1.1 INTRODUCTION; 1.2 TOWARD AUTOMATING PARALLEL PROGRAMMING; 1.3 ALGORITHMS; 1.4 PARALLEL COMPUTING DESIGN CONSIDERATIONS; 1.5 PARALLEL ALGORITHMS AND PARALLEL ARCHITECTURES; 1.6 RELATING PARALLEL ALGORITHM AND PARALLEL ARCHITECTURE; 1.7 IMPLEMENTATION OF ALGORITHMS: A TWO-SIDED PROBLEM; 1.8 MEASURING BENEFITS OF PARALLEL COMPUTING; 1.9 AMDAHL'S LAW FOR MULTIPROCESSOR SYSTEMS; 1.10 GUSTAFSON-BARSIS'S LAW; 1.11 APPLICATIONS OF PARALLEL COMPUTING; Chapter 2: Enhancing Uniprocessor Performance
2.1 INTRODUCTION2.2 INCREASING PROCESSOR CLOCK FREQUENCY; 2.3 PARALLELIZING ALU STRUCTURE; 2.4 USING MEMORY HIERARCHY; 2.5 PIPELINING; 2.6 VERY LONG INSTRUCTION WORD (VLIW) PROCESSORS; 2.7 INSTRUCTION-LEVEL PARALLELISM (ILP) AND SUPERSCALAR PROCESSORS; 2.8 MULTITHREADED PROCESSOR; Chapter 3: Parallel Computers; 3.1 INTRODUCTION; 3.2 PARALLEL COMPUTING; 3.3 SHARED-MEMORY MULTIPROCESSORS (UNIFORM MEMORY ACCESS [UMA]); 3.4 DISTRIBUTED-MEMORY MULTIPROCESSOR (NONUNIFORM MEMORY ACCESS [NUMA]); 3.5 SIMD PROCESSORS; 3.6 SYSTOLIC PROCESSORS; 3.7 CLUSTER COMPUTING; 3.8 GRID (CLOUD) COMPUTING 3.9 MULTICORE SYSTEMS3.10 SM; 3.11 COMMUNICATION BETWEEN PARALLEL PROCESSORS; 3.12 SUMMARY OF PARALLEL ARCHITECTURES; Chapter 4: Shared-Memory Multiprocessors; 4.1 INTRODUCTION; 4.2 CACHE COHERENCE AND MEMORY CONSISTENCY; 4.3 SYNCHRONIZATION AND MUTUAL EXCLUSION; Chapter 5: Interconnection Networks; 5.1 INTRODUCTION; 5.2 CLASSIFICATION OF INTERCONNECTION NETWORKS BY LOGICAL TOPOLOGIES; 5.3 INTERCONNECTION NETWORK SWITCH ARCHITECTURE; Chapter 6: Concurrency Platforms; 6.1 INTRODUCTION; 6.2 CONCURRENCY PLATFORMS; 6.3 CILK++; 6.4 OpenMP; 6.5 COMPUTE UNIFIED DEVICE ARCHITECTURE (CUDA) Chapter 7: Ad Hoc Techniques for Parallel Algorithms7.1 INTRODUCTION; 7.2 DEFINING ALGORITHM VARIABLES; 7.3 INDEPENDENT LOOP SCHEDULING; 7.4 DEPENDENT LOOPS; 7.5 LOOP SPREADING FOR SIMPLE DEPENDENT LOOPS; 7.6 LOOP UNROLLING; 7.7 PROBLEM PARTITIONING; 7.8 DIVIDE-AND-CONQUER (RECURSIVE PARTITIONING) STRATEGIES; 7.9 PIPELINING; Chapter 8: Nonserial-Parallel Algorithms; 8.1 INTRODUCTION; 8.2 COMPARING DAG AND DCG ALGORITHMS; 8.3 PARALLELIZING NSPA ALGORITHMS REPRESENTED BY A DAG; 8.4 FORMAL TECHNIQUE FOR ANALYZING NSPAs; 8.5 DETECTING CYCLES IN THE ALGORITHM 8.6 EXTRACTING SERIAL AND PARALLEL ALGORITHM PERFORMANCE PARAMETERS8.7 USEFUL THEOREMS; 8.8 PERFORMANCE OF SERIAL AND PARALLEL ALGORITHMS ON PARALLEL COMPUTERS; Chapter 9: z-Transform Analysis; 9.1 INTRODUCTION; 9.2 DEFINITION OF z-TRANSFORM; 9.3 THE 1-D FIR DIGITAL FILTER ALGORITHM; 9.4 SOFTWARE AND HARDWARE IMPLEMENTATIONS OF THE z-TRANSFORM; 9.5 DESIGN 1: USING HORNER'S RULE FOR BROADCAST INPUT AND PIPELINED OUTPUT; 9.6 DESIGN 2: PIPELINED INPUT AND BROADCAST OUTPUT; 9.7 DESIGN 3: PIPELINED INPUT AND OUTPUT; Chapter 10: Dependence Graph Analysis; 10.1 INTRODUCTION 10.2 THE 1-D FIR DIGITAL FILTER ALGORITHM |
| Record Nr. | UNINA-9910830899403321 |
|
Gebali Fayez
|
||
| Hoboken, N.J., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali
| Algorithms and parallel computing [[electronic resource] /] / Fayez Gebali |
| Autore | Gebali Fayez |
| Edizione | [1st edition] |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley, 2011 |
| Descrizione fisica | 1 online resource (365 p.) |
| Disciplina |
004.35
004/.35 005.275 |
| Collana | Wiley series on parallel and distributed computing |
| Soggetto topico |
Parallel processing (Electronic computers)
Computer algorithms |
| ISBN |
1-283-02557-4
9786613025579 0-470-93201-5 0-470-93202-3 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Algorithms and Parallel Computing; Contents; Preface; List of Acronyms; Chapter 1: Introduction; 1.1 INTRODUCTION; 1.2 TOWARD AUTOMATING PARALLEL PROGRAMMING; 1.3 ALGORITHMS; 1.4 PARALLEL COMPUTING DESIGN CONSIDERATIONS; 1.5 PARALLEL ALGORITHMS AND PARALLEL ARCHITECTURES; 1.6 RELATING PARALLEL ALGORITHM AND PARALLEL ARCHITECTURE; 1.7 IMPLEMENTATION OF ALGORITHMS: A TWO-SIDED PROBLEM; 1.8 MEASURING BENEFITS OF PARALLEL COMPUTING; 1.9 AMDAHL'S LAW FOR MULTIPROCESSOR SYSTEMS; 1.10 GUSTAFSON-BARSIS'S LAW; 1.11 APPLICATIONS OF PARALLEL COMPUTING; Chapter 2: Enhancing Uniprocessor Performance
2.1 INTRODUCTION2.2 INCREASING PROCESSOR CLOCK FREQUENCY; 2.3 PARALLELIZING ALU STRUCTURE; 2.4 USING MEMORY HIERARCHY; 2.5 PIPELINING; 2.6 VERY LONG INSTRUCTION WORD (VLIW) PROCESSORS; 2.7 INSTRUCTION-LEVEL PARALLELISM (ILP) AND SUPERSCALAR PROCESSORS; 2.8 MULTITHREADED PROCESSOR; Chapter 3: Parallel Computers; 3.1 INTRODUCTION; 3.2 PARALLEL COMPUTING; 3.3 SHARED-MEMORY MULTIPROCESSORS (UNIFORM MEMORY ACCESS [UMA]); 3.4 DISTRIBUTED-MEMORY MULTIPROCESSOR (NONUNIFORM MEMORY ACCESS [NUMA]); 3.5 SIMD PROCESSORS; 3.6 SYSTOLIC PROCESSORS; 3.7 CLUSTER COMPUTING; 3.8 GRID (CLOUD) COMPUTING 3.9 MULTICORE SYSTEMS3.10 SM; 3.11 COMMUNICATION BETWEEN PARALLEL PROCESSORS; 3.12 SUMMARY OF PARALLEL ARCHITECTURES; Chapter 4: Shared-Memory Multiprocessors; 4.1 INTRODUCTION; 4.2 CACHE COHERENCE AND MEMORY CONSISTENCY; 4.3 SYNCHRONIZATION AND MUTUAL EXCLUSION; Chapter 5: Interconnection Networks; 5.1 INTRODUCTION; 5.2 CLASSIFICATION OF INTERCONNECTION NETWORKS BY LOGICAL TOPOLOGIES; 5.3 INTERCONNECTION NETWORK SWITCH ARCHITECTURE; Chapter 6: Concurrency Platforms; 6.1 INTRODUCTION; 6.2 CONCURRENCY PLATFORMS; 6.3 CILK++; 6.4 OpenMP; 6.5 COMPUTE UNIFIED DEVICE ARCHITECTURE (CUDA) Chapter 7: Ad Hoc Techniques for Parallel Algorithms7.1 INTRODUCTION; 7.2 DEFINING ALGORITHM VARIABLES; 7.3 INDEPENDENT LOOP SCHEDULING; 7.4 DEPENDENT LOOPS; 7.5 LOOP SPREADING FOR SIMPLE DEPENDENT LOOPS; 7.6 LOOP UNROLLING; 7.7 PROBLEM PARTITIONING; 7.8 DIVIDE-AND-CONQUER (RECURSIVE PARTITIONING) STRATEGIES; 7.9 PIPELINING; Chapter 8: Nonserial-Parallel Algorithms; 8.1 INTRODUCTION; 8.2 COMPARING DAG AND DCG ALGORITHMS; 8.3 PARALLELIZING NSPA ALGORITHMS REPRESENTED BY A DAG; 8.4 FORMAL TECHNIQUE FOR ANALYZING NSPAs; 8.5 DETECTING CYCLES IN THE ALGORITHM 8.6 EXTRACTING SERIAL AND PARALLEL ALGORITHM PERFORMANCE PARAMETERS8.7 USEFUL THEOREMS; 8.8 PERFORMANCE OF SERIAL AND PARALLEL ALGORITHMS ON PARALLEL COMPUTERS; Chapter 9: z-Transform Analysis; 9.1 INTRODUCTION; 9.2 DEFINITION OF z-TRANSFORM; 9.3 THE 1-D FIR DIGITAL FILTER ALGORITHM; 9.4 SOFTWARE AND HARDWARE IMPLEMENTATIONS OF THE z-TRANSFORM; 9.5 DESIGN 1: USING HORNER'S RULE FOR BROADCAST INPUT AND PIPELINED OUTPUT; 9.6 DESIGN 2: PIPELINED INPUT AND BROADCAST OUTPUT; 9.7 DESIGN 3: PIPELINED INPUT AND OUTPUT; Chapter 10: Dependence Graph Analysis; 10.1 INTRODUCTION 10.2 THE 1-D FIR DIGITAL FILTER ALGORITHM |
| Record Nr. | UNINA-9910841043903321 |
|
Gebali Fayez
|
||
| Hoboken, N.J., : Wiley, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Energy efficient distributed computing systems / / edited by Albert Y. Zomaya, Young Choon Lee
| Energy efficient distributed computing systems / / edited by Albert Y. Zomaya, Young Choon Lee |
| Autore | Zomaya Albert Y |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , c2012 |
| Descrizione fisica | 1 online resource (855 p.) |
| Disciplina | 004/.36 |
| Altri autori (Persone) |
ZomayaAlbert Y
LeeYoung-Choon <1973-> |
| Collana |
Wiley series on parallel and distributed computing
Wiley series on parallel and distributed computing |
| Soggetto topico |
Computer networks - Energy conservation
Electronic data processing - Distributed processing - Energy conservation Green technology |
| ISBN |
1-283-54601-9
9786613858467 1-118-34201-1 1-118-34200-3 1-118-34198-8 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE xxix -- ACKNOWLEDGMENTS xxxi -- CONTRIBUTORS xxxiii -- 1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSORCOMPUTERS WITH ENERGY AND TIME CONSTRAINTS 1 / Keqin Li -- 1.1 Introduction 1 -- 1.1.1 Energy Consumption 1 -- 1.1.2 Power Reduction 2 -- 1.1.3 Dynamic Power Management 3 -- 1.1.4 Task Scheduling with Energy and Time Constraints 4 -- 1.1.5 Chapter Outline 5 -- 1.2 Preliminaries 5 -- 1.2.1 Power Consumption Model 5 -- 1.2.2 Problem Definitions 6 -- 1.2.3 Task Models 7 -- 1.2.4 Processor Models 8 -- 1.2.5 Scheduling Models 9 -- 1.2.6 Problem Decomposition 9 -- 1.2.7 Types of Algorithms 10 -- 1.3 Problem Analysis 10 -- 1.3.1 Schedule Length Minimization 10 -- 1.3.1.1 Uniprocessor computers 10 -- 1.3.1.2 Multiprocessor computers 11 -- 1.3.2 Energy Consumption Minimization 12 -- 1.3.2.1 Uniprocessor computers 12 -- 1.3.2.2 Multiprocessor computers 13 -- 1.3.3 Strong NP-Hardness 14 -- 1.3.4 Lower Bounds 14 -- 1.3.5 Energy-Delay Trade-off 15 -- 1.4 Pre-Power-Determination Algorithms 16 -- 1.4.1 Overview 16 -- 1.4.2 Performance Measures 17 -- 1.4.3 Equal-Time Algorithms and Analysis 18 -- 1.4.3.1 Schedule length minimization 18 -- 1.4.3.2 Energy consumption minimization 19 -- 1.4.4 Equal-Energy Algorithms and Analysis 19 -- 1.4.4.1 Schedule length minimization 19 -- 1.4.4.2 Energy consumption minimization 21 -- 1.4.5 Equal-Speed Algorithms and Analysis 22 -- 1.4.5.1 Schedule length minimization 22 -- 1.4.5.2 Energy consumption minimization 23 -- 1.4.6 Numerical Data 24 -- 1.4.7 Simulation Results 25 -- 1.5 Post-Power-Determination Algorithms 28 -- 1.5.1 Overview 28 -- 1.5.2 Analysis of List Scheduling Algorithms 29 -- 1.5.2.1 Analysis of algorithm LS 29 -- 1.5.2.2 Analysis of algorithm LRF 30 -- 1.5.3 Application to Schedule Length Minimization 30 -- 1.5.4 Application to Energy Consumption Minimization 31 -- 1.5.5 Numerical Data 32 -- 1.5.6 Simulation Results 32 -- 1.6 Summary and Further Research 33 -- References 34 -- 2 POWER-AWARE HIGH PERFORMANCE COMPUTING 39 / Rong Ge and Kirk W. Cameron.
2.1 Introduction 39 -- 2.2 Background 41 -- 2.2.1 Current Hardware Technology and Power Consumption 41 -- 2.2.1.1 Processor power 41 -- 2.2.1.2 Memory subsystem power 42 -- 2.2.2 Performance 43 -- 2.2.3 Energy Efficiency 44 -- 2.3 Related Work 45 -- 2.3.1 Power Profiling 45 -- 2.3.1.1 Simulator-based power estimation 45 -- 2.3.1.2 Direct measurements 46 -- 2.3.1.3 Event-based estimation 46 -- 2.3.2 Performance Scalability on Power-Aware Systems 46 -- 2.3.3 Adaptive Power Allocation for Energy-Efficient Computing47 -- 2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications48 -- 2.4.1 Design and Implementation of PowerPack 48 -- 2.4.1.1 Overview 48 -- 2.4.1.2 Fine-grain systematic power measurement 50 -- 2.4.1.3 Automatic power profiling and code synchronization51 -- 2.4.2 Power Profiles of HPC Applications and Systems 53 -- 2.4.2.1 Power distribution over components 53 -- 2.4.2.2 Power dynamics of applications 54 -- 2.4.2.3 Power bounds on HPC systems 55 -- 2.4.2.4 Power versus dynamic voltage and frequency scaling57 -- 2.5 Power-Aware Speedup Model 59 -- 2.5.1 Power-Aware Speedup 59 -- 2.5.1.1 Sequential execution time for a single workload T1(w, f) 60 -- 2.5.1.2 Sequential execution time for an ON-chip/OFF-chipworkload 60 -- 2.5.1.3 Parallel execution time on N processors for anON-/OFF-chip workload with DOP = i 61 -- 2.5.1.4 Power-aware speedup for DOP and ON-/OFF-chip workloads62 -- 2.5.2 Model Parametrization and Validation 63 -- 2.5.2.1 Coarse-grain parametrization and validation 64 -- 2.5.2.2 Fine-grain parametrization and validation 66 -- 2.6 Model Usages 69 -- 2.6.1 Identification of Optimal System Configurations 70 -- 2.6.2 PAS-Directed Energy-Driven Runtime Frequency Scaling71 -- 2.7 Conclusion 73 -- References 75 -- 3 ENERGY EFFICIENCY IN HPC SYSTEMS 81 / Ivan Rodero and Manish Parashar -- 3.1 Introduction 81 -- 3.2 Background and Related Work 83 -- 3.2.1 CPU Power Management 83 -- 3.2.1.1 OS-level CPU power management 83 -- 3.2.1.2 Workload-level CPU power management 84. 3.2.1.3 Cluster-level CPU power management 84 -- 3.2.2 Component-Based Power Management 85 -- 3.2.2.1 Memory subsystem 85 -- 3.2.2.2 Storage subsystem 86 -- 3.2.3 Thermal-Conscious Power Management 87 -- 3.2.4 Power Management in Virtualized Datacenters 87 -- 3.3 Proactive, Component-Based Power Management 88 -- 3.3.1 Job Allocation Policies 88 -- 3.3.2 Workload Profiling 90 -- 3.4 Quantifying Energy Saving Possibilities 91 -- 3.4.1 Methodology 92 -- 3.4.2 Component-Level Power Requirements 92 -- 3.4.3 Energy Savings 94 -- 3.5 Evaluation of the Proposed Strategies 95 -- 3.5.1 Methodology 96 -- 3.5.2 Workloads 96 -- 3.5.3 Metrics 97 -- 3.6 Results 97 -- 3.7 Concluding Remarks 102 -- 3.8 Summary 103 -- References 104 -- 4 A STOCHASTIC FRAMEWORK FOR HIERARCHICAL SYSTEM-LEVEL POWERMANAGEMENT 109 / Peng Rong and Massoud Pedram -- 4.1 Introduction 109 -- 4.2 Related Work 111 -- 4.3 A Hierarchical DPM Architecture 113 -- 4.4 Modeling 114 -- 4.4.1 Model of the Application Pool 114 -- 4.4.2 Model of the Service Flow Control 118 -- 4.4.3 Model of the Simulated Service Provider 119 -- 4.4.4 Modeling Dependencies between SPs 120 -- 4.5 Policy Optimization 122 -- 4.5.1 Mathematical Formulation 122 -- 4.5.2 Optimal Time-Out Policy for Local Power Manager 123 -- 4.6 Experimental Results 125 -- 4.7 Conclusion 130 -- References 130 -- 5 ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE FOR GRIDS,CLOUDS, AND NETWORKS 133 / Anne-Ce' cile Orgerie and Laurent Lefe` vre -- 5.1 Introduction 133 -- 5.2 Related Works 134 -- 5.2.1 Server and Data Center Power Management 135 -- 5.2.2 Node Optimizations 135 -- 5.2.3 Virtualization to Improve Energy Efficiency 136 -- 5.2.4 Energy Awareness in Wired Networking Equipment 136 -- 5.2.5 Synthesis 137 -- 5.3 ERIDIS: Energy-Efficient Reservation Infrastructure forLarge-Scale Distributed Systems 138 -- 5.3.1 ERIDIS Architecture 138 -- 5.3.2 Management of the Resource Reservations 141 -- 5.3.3 Resource Management and On/Off Algorithms 145 -- 5.3.4 Energy-Consumption Estimates 146. 5.3.5 Prediction Algorithms 146 -- 5.4 EARI: Energy-Aware Reservation Infrastructure for DataCenters and Grids 147 -- 5.4.1 EARI's Architecture 147 -- 5.4.2 Validation of EARI on Experimental Grid Traces 147 -- 5.5 GOC: Green Open Cloud 149 -- 5.5.1 GOC's Resource Manager Architecture 150 -- 5.5.2 Validation of the GOC Framework 152 -- 5.6 HERMES: High Level Energy-Aware Model for BandwidthReservation in End-To-End Networks 152 -- 5.6.1 HERMES' Architecture 154 -- 5.6.2 The Reservation Process of HERMES 155 -- 5.6.3 Discussion 157 -- 5.7 Summary 158 -- References 158 -- 6 ENERGY-EFFICIENT JOB PLACEMENT ON CLUSTERS, GRIDS, ANDCLOUDS 163 / Damien Borgetto, Henri Casanova, Georges Da Costa, andJean-Marc Pierson -- 6.1 Problem and Motivation 163 -- 6.1.1 Context 163 -- 6.1.2 Chapter Roadmap 164 -- 6.2 Energy-Aware Infrastructures 164 -- 6.2.1 Buildings 165 -- 6.2.2 Context-Aware Buildings 165 -- 6.2.3 Cooling 166 -- 6.3 Current Resource Management Practices 167 -- 6.3.1 Widely Used Resource Management Systems 167 -- 6.3.2 Job Requirement Description 169 -- 6.4 Scientific and Technical Challenges 170 -- 6.4.1 Theoretical Difficulties 170 -- 6.4.2 Technical Difficulties 170 -- 6.4.3 Controlling and Tuning Jobs 171 -- 6.5 Energy-Aware Job Placement Algorithms 172 -- 6.5.1 State of the Art 172 -- 6.5.2 Detailing One Approach 174 -- 6.6 Discussion 180 -- 6.6.1 Open Issues and Opportunities 180 -- 6.6.2 Obstacles for Adoption in Production 182 -- 6.7 Conclusion 183 -- References 184 -- 7 COMPARISON AND ANALYSIS OF GREEDY ENERGY-EFFICIENTSCHEDULING ALGORITHMS FOR COMPUTATIONAL GRIDS 189 / Peder Lindberg, James Leingang, Daniel Lysaker, KashifBilal, Samee Ullah Khan, Pascal Bouvry, Nasir Ghani, NasroMin-Allah, and Juan Li -- 7.1 Introduction 189 -- 7.2 Problem Formulation 191 -- 7.2.1 The System Model 191 -- 7.2.1.1 PEs 191 -- 7.2.1.2 DVS 191 -- 7.2.1.3 Tasks 192 -- 7.2.1.4 Preliminaries 192 -- 7.2.2 Formulating the Energy-Makespan Minimization Problem192 -- 7.3 Proposed Algorithms 193. 7.3.1 Greedy Heuristics 194 -- 7.3.1.1 Greedy heuristic scheduling algorithm 196 -- 7.3.1.2 Greedy-min 197 -- 7.3.1.3 Greedy-deadline 198 -- 7.3.1.4 Greedy-max 198 -- 7.3.1.5 MaxMin 199 -- 7.3.1.6 ObFun 199 -- 7.3.1.7 MinMin StdDev 202 -- 7.3.1.8 MinMax StdDev 202 -- 7.4 Simulations, Results, and Discussion 203 -- 7.4.1 Workload 203 -- 7.4.2 Comparative Results 204 -- 7.4.2.1 Small-size problems 204 -- 7.4.2.2 Large-size problems 206 -- 7.5 Related Works 211 -- 7.6 Conclusion 211 -- References 212 -- 8 TOWARD ENERGY-AWARE SCHEDULING USING MACHINE LEARNING215 / Josep LL. Berral, In igo Goiri, Ramon Nou, FerranJulia` , Josep O. Fito' , Jordi Guitart, Ricard Gavalda', and Jordi Torres -- 8.1 Introduction 215 -- 8.1.1 Energetic Impact of the Cloud 216 -- 8.1.2 An Intelligent Way to Manage Data Centers 216 -- 8.1.3 Current Autonomic Computing Techniques 217 -- 8.1.4 Power-Aware Autonomic Computing 217 -- 8.1.5 State of the Art and Case Study 218 -- 8.2 Intelligent Self-Management 218 -- 8.2.1 Classical AI Approaches 219 -- 8.2.1.1 Heuristic algorithms 219 -- 8.2.1.2 AI planning 219 -- 8.2.1.3 Semantic techniques 219 -- 8.2.1.4 Expert systems and genetic algorithms 220 -- 8.2.2 Machine Learning Approaches 220 -- 8.2.2.1 Instance-based learning 221 -- 8.2.2.2 Reinforcement learning 222 -- 8.2.2.3 Feature and example selection 225 -- 8.3 Introducing Power-Aware Approaches 225 -- 8.3.1 Use of Virtualization 226 -- 8.3.2 Turning On and Off Machines 228 -- 8.3.3 Dynamic Voltage and Frequency Scaling 229 -- 8.3.4 Hybrid Nodes and Data Centers 230 -- 8.4 Experiences of Applying ML on Power-Aware Self-Management230 -- 8.4.1 Case Study Approach 231 -- 8.4.2 Scheduling and Power Trade-Off 231 -- 8.4.3 Experimenting with Power-Aware Techniques 233 -- 8.4.4 Applying Machine Learning 236 -- 8.4.5 Conclusions from the Experiments 238 -- 8.5 Conclusions on Intelligent Power-Aware Self-Management238 -- References 240 -- 9 ENERGY EFFICIENCY METRICS FOR DATA CENTERS 245 / Javid Taheri and Albert Y. Zomaya. 9.1 Introduction 245 -- 9.1.1 Background 245 -- 9.1.2 Data Center Energy Use 246 -- 9.1.3 Data Center Characteristics 246 -- 9.1.3.1 Electric power 247 -- 9.1.3.2 Heat removal 249 -- 9.1.4 Energy Efficiency 250 -- 9.2 Fundamentals of Metrics 250 -- 9.2.1 Demand and Constraints on Data Center Operators 250 -- 9.2.2 Metrics 251 -- 9.2.2.1 Criteria for good metrics 251 -- 9.2.2.2 Methodology 252 -- 9.2.2.3 Stability of metrics 252 -- 9.3 Data Center Energy Efficiency 252 -- 9.3.1 Holistic IT Efficiency Metrics 252 -- 9.3.1.1 Fixed versus proportional overheads 254 -- 9.3.1.2 Power versus energy 254 -- 9.3.1.3 Performance versus productivity 255 -- 9.3.2 Code of Conduct 256 -- 9.3.2.1 Environmental statement 256 -- 9.3.2.2 Problem statement 256 -- 9.3.2.3 Scope of the CoC 257 -- 9.3.2.4 Aims and objectives of CoC 258 -- 9.3.3 Power Use in Data Centers 259 -- 9.3.3.1 Data center IT power to utility power relationship259 -- 9.3.3.2 Chiller efficiency and external temperature 260 -- 9.4 Available Metrics 260 -- 9.4.1 The Green Grid 261 -- 9.4.1.1 Power usage effectiveness (PUE) 261 -- 9.4.1.2 Data center efficiency (DCE) 262 -- 9.4.1.3 Data center infrastructure efficiency (DCiE) 262 -- 9.4.1.4 Data center productivity (DCP) 263 -- 9.4.2 McKinsey 263 -- 9.4.3 Uptime Institute 264 -- 9.4.3.1 Site infrastructure power overhead multiplier (SI-POM)265 -- 9.4.3.2 IT hardware power overhead multiplier (H-POM) 266 -- 9.4.3.3 DC hardware compute load per unit of computing work done266 -- 9.4.3.4 Deployed hardware utilization ratio (DH-UR) 266 -- 9.4.3.5 Deployed hardware utilization efficiency (DH-UE) 267 -- 9.5 Harmonizing Global Metrics for Data Center Energy Efficiency267 -- References 268 -- 10 AUTONOMIC GREEN COMPUTING IN LARGE-SCALE DATA CENTERS271 / Haoting Luo, Bithika Khargharia, Salim Hariri, and YoussifAl-Nashif / -- 10.1 Introduction 271 -- 10.2 Related Technologies and Techniques 272 -- 10.2.1 Power Optimization Techniques in Data Centers 272 -- 10.2.2 Design Model 273. 10.2.3 Networks 274 -- 10.2.4 Data Center Power Distribution 275 -- 10.2.5 Data Center Power-Efficient Metrics 276 -- 10.2.6 Modeling Prototype and Testbed 277 -- 10.2.7 Green Computing 278 -- 10.2.8 Energy Proportional Computing 280 -- 10.2.9 Hardware Virtualization Technology 281 -- 10.2.10 Autonomic Computing 282 -- 10.3 Autonomic Green Computing: A Case Study 283 -- 10.3.1 Autonomic Management Platform 285 -- 10.3.1.1 Platform architecture 285 -- 10.3.1.2 DEVS-based modeling and simulation platform 285 -- 10.3.1.3 Workload generator 287 -- 10.3.2 Model Parameter Evaluation 288 -- 10.3.2.1 State transitioning overhead 288 -- 10.3.2.2 VM template evaluation 289 -- 10.3.2.3 Scalability analysis 291 -- 10.3.3 Autonomic Power Efficiency Management Algorithm(Performance Per Watt) 291 -- 10.3.4 Simulation Results and Evaluation 293 -- 10.3.4.1 Analysis of energy and performance trade-offs 296 -- 10.4 Conclusion and Future Directions 297 -- References 298 -- 11 ENERGY AND THERMAL AWARE SCHEDULING IN DATA CENTERS301 / Gaurav Dhiman, Raid Ayoub, and Tajana S. Rosing -- 11.1 Introduction 301 -- 11.2 Related Work 302 -- 11.3 Intermachine Scheduling 305 -- 11.3.1 Performance and Power Profile of VMs 305 -- 11.3.2 Architecture 309 -- 11.3.2.1 vgnode 309 -- 11.3.2.2 vgxen 310 -- 11.3.2.3 vgdom 312 -- 11.3.2.4 vgserv 312 -- 11.4 Intramachine Scheduling 315 -- 11.4.1 Air-Forced Thermal Modeling and Cost 316 -- 11.4.2 Cooling Aware Dynamic Workload Scheduling 317 -- 11.4.3 Scheduling Mechanism 318 -- 11.4.4 Cooling Costs Predictor 319 -- 11.5 Evaluation 321 -- 11.5.1 Intermachine Scheduler (vGreen) 321 -- 11.5.2 Heterogeneous Workloads 323 -- 11.5.2.1 Comparison with DVFS policies 325 -- 11.5.2.2 Homogeneous workloads 328 -- 11.5.3 Intramachine Scheduler (Cool and Save) 328 -- 11.5.3.1 Results 331 -- 11.5.3.2 Overhead of CAS 333 -- 11.6 Conclusion 333 -- References 334 -- 12 QOS-AWARE POWER MANAGEMENT IN DATA CENTERS 339 / Jiayu Gong and Cheng-Zhong Xu -- 12.1 Introduction 339. 12.2 Problem Classification 340 -- 12.2.1 Objective and Constraint 340 -- 12.2.2 Scope and Time Granularities 340 -- 12.2.3 Methodology 341 -- 12.2.4 Power Management Mechanism 342 -- 12.3 Energy Efficiency 344 -- 12.3.1 Energy-Efficiency Metrics 344 -- 12.3.2 Improving Energy Efficiency 346 -- 12.3.2.1 Energy minimization with performance guarantee 346 -- 12.3.2.2 Performance maximization under power budget 348 -- 12.3.2.3 Trade-off between power and performance 348 -- 12.3.3 Energy-Proportional Computing 350 -- 12.4 Power Capping 351 -- 12.5 Conclusion 353 -- References 356 -- 13 ENERGY-EFFICIENT STORAGE SYSTEMS FOR DATA CENTERS361 / Sudhanva Gurumurthi and Anand Sivasubramaniam -- 13.1 Introduction 361 -- 13.2 Disk Drive Operation and Disk Power 362 -- 13.2.1 An Overview of Disk Drives 362 -- 13.2.2 Sources of Disk Power Consumption 363 -- 13.2.3 Disk Activity and Power Consumption 365 -- 13.3 Disk and Storage Power Reduction Techniques 366 -- 13.3.1 Exploiting the STANDBY State 368 -- 13.3.2 Reducing Seek Activity 369 -- 13.3.3 Achieving Energy Proportionality 369 -- 13.3.3.1 Hardware approaches 369 -- 13.3.3.2 Software approaches 370 -- 13.4 Using Nonvolatile Memory and Solid-State Disks 371 -- 13.5 Conclusions 372 -- References 373 -- 14 AUTONOMIC ENERGY/PERFORMANCE OPTIMIZATIONS FOR MEMORY INSERVERS 377 / Bithika Khargharia and Mazin Yousif -- 14.1 Introduction 378 -- 14.2 Classifications of Dynamic Power Management Techniques380 -- 14.2.1 Heuristic and Predictive Techniques 380 -- 14.2.2 QoS and Energy Trade-Offs 381 -- 14.3 Applications of Dynamic Power Management (DPM) 382 -- 14.3.1 Power Management of System Components in Isolation382 -- 14.3.2 Joint Power Management of System Components 383 -- 14.3.3 Holistic System-Level Power Management 383 -- 14.4 Autonomic Power and Performance Optimization of MemorySubsystems in Server Platforms 384 -- 14.4.1 Adaptive Memory Interleaving Technique for Power andPerformance Management 384 -- 14.4.1.1 Formulating the optimization problem 386. 14.4.1.2 Memory appflow 389 -- 14.4.2 Industry Techniques 389 -- 14.4.2.1 Enhancements in memory hardware design 390 -- 14.4.2.2 Adding more operating states 390 -- 14.4.2.3 Faster transition to and from low power states 390 -- 14.4.2.4 Memory consolidation 390 -- 14.5 Conclusion 391 -- References 391 -- 15 ROD: A PRACTICAL APPROACH TO IMPROVING RELIABILITY OFENERGY-EFFICIENT PARALLEL DISK SYSTEMS 395 / Shu Yin, Xiaojun Ruan, Adam Manzanares, and XiaoQin -- 15.1 Introduction 395 -- 15.2 Modeling Reliability of Energy-Efficient Parallel Disks396 -- 15.2.1 The MINT Model 396 -- 15.2.1.1 Disk utilization 398 -- 15.2.1.2 Temperature 398 -- 15.2.1.3 Power-state transition frequency 399 -- 15.2.1.4 Single disk reliability model 399 -- 15.2.2 MAID, Massive Arrays of Idle Disks 400 -- 15.3 Improving Reliability of MAID via Disk Swapping 401 -- 15.3.1 Improving Reliability of Cache Disks in MAID 401 -- 15.3.2 Swapping Disks Multiple Times 404 -- 15.4 Experimental Results and Evaluation 405 -- 15.4.1 Experimental Setup 405 -- 15.4.2 Disk Utilization 406 -- 15.4.3 The Single Disk Swapping Strategy 406 -- 15.4.4 The Multiple Disk Swapping Strategy 409 -- 15.5 Related Work 411 -- 15.6 Conclusions 412 -- References 413 -- 16 EMBRACING THE MEMORY AND I/O WALLS FOR ENERGY-EFFICIENTSCIENTIFIC COMPUTING 417 / Chung-Hsing Hsu and Wu-Chun Feng -- 16.1 Introduction 417 -- 16.2 Background and Related Work 420 -- 16.2.1 DVFS-Enabled Processors 420 -- 16.2.2 DVFS Scheduling Algorithms 421 -- 16.2.3 Memory-Aware, Interval-Based Algorithms 422 -- 16.3 β-Adaptation: A New DVFS Algorithm 423 -- 16.3.1 The Compute-Boundedness Metric, β 423 -- 16.3.2 The Frequency Calculating Formula, f ∗ 424 -- 16.3.3 The Online β Estimation 425 -- 16.3.4 Putting It All Together 427 -- 16.4 Algorithm Effectiveness 429 -- 16.4.1 A Comparison to Other DVFS Algorithms 429 -- 16.4.2 Frequency Emulation 432 -- 16.4.3 The Minimum Dependence to the PMU 436 -- 16.5 Conclusions and Future Work 438 -- References 439. 17 MULTIPLE FREQUENCY SELECTION IN DVFS-ENABLED PROCESSORS TOMINIMIZE ENERGY CONSUMPTION 443 / Nikzad Babaii Rizvandi, Albert Y. Zomaya, Young Choon Lee,Ali Javadzadeh Boloori, and Javid Taheri -- 17.1 Introduction 443 -- 17.2 Energy Efficiency in HPC Systems 444 -- 17.3 Exploitation of Dynamic Voltage-Frequency Scaling446 -- 17.3.1 Independent Slack Reclamation 446 -- 17.3.2 Integrated Schedule Generation 447 -- 17.4 Preliminaries 448 -- 17.4.1 System and Application Models 448 -- 17.4.2 Energy Model 448 -- 17.5 Energy-Aware Scheduling via DVFS 450 -- 17.5.1 Optimum Continuous Frequency 450 -- 17.5.2 Reference Dynamic Voltage-Frequency Scaling (RDVFS)451 -- 17.5.3 Maximum-Minimum-Frequency for DynamicVoltage-Frequency Scaling (MMF-DVFS) 452 -- 17.5.4 Multiple Frequency Selection for DynamicVoltage-Frequency Scaling (MFS-DVFS) 453 -- 17.5.4.1 Task eligibility 454 -- 17.6 Experimental Results 456 -- 17.6.1 Simulation Settings 456 -- 17.6.2 Results 458 -- 17.7 Conclusion 461 -- References 461 -- 18 THE PARAMOUNTCY OF RECONFIGURABLE COMPUTING 465 / Reiner Hartenstein -- 18.1 Introduction 465 -- 18.2 Why Computers are Important 466 -- 18.2.1 Computing for a Sustainable Environment 470 -- 18.3 Performance Progress Stalled 472 -- 18.3.1 Unaffordable Energy Consumption of Computing 473 -- 18.3.2 Crashing into the Programming Wall 475 -- 18.4 The Tail is Wagging the Dog (Accelerators) 488 -- 18.4.1 Hardwired Accelerators 489 -- 18.4.2 Programmable Accelerators 490 -- 18.5 Reconfigurable Computing 494 -- 18.5.1 Speedup Factors by FPGAs 498 -- 18.5.2 The Reconfigurable Computing Paradox 501 -- 18.5.3 Saving Energy by Reconfigurable Computing 505 -- 18.5.3.1 Traditional green computing 506 -- 18.5.3.2 The role of graphics processors 507 -- 18.5.3.3 Wintel versus ARM 508 -- 18.5.4 Reconfigurable Computing is the Silver Bullet 511 -- 18.5.4.1 A new world model of computing 511 -- 18.5.5 The Twin-Paradigm Approach to Tear Down the Wall 514 -- 18.5.6 A Mass Movement Needed as Soon as Possible 517. 18.5.6.1 Legacy software from the mainframe age 518 -- 18.5.7 How to Reinvent Computing 519 18.6 Conclusions 526 -- References 529 -- 19 WORKLOAD CLUSTERING FOR INCREASING ENERGY SAVINGS ONEMBEDDED MPSOCS 549 / Ozcan Ozturk, Mahmut Kandemir, and Sri Hari KrishnaNarayanan -- 19.1 Introduction 549 -- 19.2 Embedded MPSoC Architecture, Execution Model, and RelatedWork 550 -- 19.3 Our Approach 551 -- 19.3.1 Overview 551 -- 19.3.2 Technical Details and Problem Formulation 553 -- 19.3.2.1 System and job model 553 -- 19.3.2.2 Mathematical programing model 554 -- 19.3.2.3 Example 557 -- 19.4 Experimental Evaluation 560 -- 19.5 Conclusions 564 -- References 565 -- 20 ENERGY-EFFICIENT INTERNET INFRASTRUCTURE 567 / Weirong Jiang and Viktor K. Prasanna -- 20.1 Introduction 567 -- 20.1.1 Performance Challenges 568 -- 20.1.2 Existing Packet Forwarding Approaches 570 -- 20.1.2.1 Software approaches 570 -- 20.1.2.2 Hardware approaches 571 -- 20.2 SRAM-Based Pipelined IP Lookup Architectures: Alternativeto TCAMs 571 -- 20.3 Data Structure Optimization for Power Efficiency 573 -- 20.3.1 Problem Formulation 574 -- 20.3.1.1 Non-pipelined and pipelined engines 574 -- 20.3.1.2 Power function of SRAM 575 -- 20.3.2 Special Case: Uniform Stride 576 -- 20.3.3 Dynamic Programming 576 -- 20.3.4 Performance Evaluation 577 -- 20.3.4.1 Results for non-pipelined architecture 578 -- 20.3.4.2 Results for pipelined architecture 578 -- 20.4 Architectural Optimization to Reduce Dynamic PowerDissipation 580 -- 20.4.1 Analysis and Motivation 581 -- 20.4.1.1 Traffic locality 582 -- 20.4.1.2 Traffic rate variation 582 -- 20.4.1.3 Access frequency on different stages 583 -- 20.4.2 Architecture-Specific Techniques 583 -- 20.4.2.1 Inherent caching 584 -- 20.4.2.2 Local clocking 584 -- 20.4.2.3 Fine-grained memory enabling 585 -- 20.4.3 Performance Evaluation 585 -- 20.5 Related Work 588 -- 20.6 Summary 589 -- References 589 -- 21 DEMAND RESPONSE IN THE SMART GRID: A DISTRIBUTED COMPUTINGPERSPECTIVE 593 / Chen Wang and Martin De Groot. 21.1 Introduction 593 -- 21.2 Demand Response 595 -- 21.2.1 Existing Demand Response Programs 595 -- 21.2.2 Demand Response Supported by the Smart Grid 597 -- 21.3 Demand Response as a Distributed System 600 -- 21.3.1 An Overlay Network for Demand Response 600 -- 21.3.2 Event Driven Demand Response 602 -- 21.3.3 Cost Driven Demand Response 604 -- 21.3.4 A Decentralized Demand Response Framework 609 -- 21.3.5 Accountability of Coordination Decision Making 610 -- 21.4 Summary 611 -- References 611 -- 22 RESOURCE MANAGEMENT FOR DISTRIBUTED MOBILE COMPUTING615 / Jong-Kook Kim -- 22.1 Introduction 615 -- 22.2 Single-Hop Energy-Constrained Environment 617 -- 22.2.1 System Model 617 -- 22.2.2 Related Work 620 -- 22.2.3 Heuristic Descriptions 621 -- 22.2.3.1 Mapping event 621 -- 22.2.3.2 Scheduling communications 621 -- 22.2.3.3 Opportunistic load balancing and minimum energy greedyheuristics 622 -- 22.2.3.4 ME-MC heuristic 622 -- 22.2.3.5 ME-ME heuristic 624 -- 22.2.3.6 CRME heuristic 625 -- 22.2.3.7 Originator and random 626 -- 22.2.3.8 Upper bound 626 -- 22.2.4 Simulation Model 628 -- 22.2.5 Results 630 -- 22.2.6 Summary 634 -- 22.3 Multihop Distributed Mobile Computing Environment 635 -- 22.3.1 The Multihop System Model 635 -- 22.3.2 Energy-Aware Routing Protocol 636 -- 22.3.2.1 Overview 636 -- 22.3.2.2 DSDV 637 -- 22.3.2.3 DSDV remaining energy 637 -- 22.3.2.4 DSDV-energy consumption per remaining energy 637 -- 22.3.3 Heuristic Description 638 -- 22.3.3.1 Random 638 -- 22.3.3.2 Estimated minimum total energy (EMTE) 638 -- 22.3.3.3 K-percent-speed (KPS) and K-percent-energy (KPE)639 -- 22.3.3.4 Energy ratio and distance (ERD) 639 -- 22.3.3.5 ETC and distance (ETCD) 640 -- 22.3.3.6 Minimum execution time (MET) 640 -- 22.3.3.7 Minimum completion time (MCT) and minimum completiontime with DVS (MCT-DVS) 640 -- 22.3.3.8 Switching algorithm (SA) 640 -- 22.3.4 Simulation Model 641 -- 22.3.5 Results 643 -- 22.3.5.1 Distributed resource management 643 -- 22.3.5.2 Energy-aware protocol 644. 22.3.6 Summary 644 -- 22.4 Future Work 647 -- References 647 -- 23 AN ENERGY-AWARE FRAMEWORK FOR MOBILE DATA MINING 653 / Carmela Comito, Domenico Talia, and Paolo Trunfio -- 23.1 Introduction 653 -- 23.2 System Architecture 654 -- 23.3 Mobile Device Components 657 -- 23.4 Energy Model 659 -- 23.5 Clustering Scheme 664 -- 23.5.1 Clustering the M2M Architecture 666 -- 23.6 Conclusion 670 -- References 670 -- 24 ENERGY AWARENESS AND EFFICIENCY IN WIRELESS SENSORNETWORKS: FROM PHYSICAL DEVICES TO THE COMMUNICATION LINK 673 / Fla' via C. Delicato and Paulo F. Pires -- 24.1 Introduction 673 -- 24.2 WSN and Power Dissipation Models 676 -- 24.2.1 Network and Node Architecture 676 -- 24.2.2 Sources of Power Dissipation in WSNs 679 -- 24.3 Strategies for Energy Optimization 683 -- 24.3.1 Intranode Level 684 -- 24.3.1.1 Duty cycling 685 -- 24.3.1.2 Adaptive sensing 691 -- 24.3.1.3 Dynamic voltage scale (DVS) 693 -- 24.3.1.4 OS task scheduling 694 -- 24.3.2 Internode Level 695 -- 24.3.2.1 Transmission power control 695 -- 24.3.2.2 Dynamic modulation scaling 696 -- 24.3.2.3 Link layer optimizations 698 -- 24.4 Final Remarks 701 -- References 702 -- 25 NETWORK-WIDE STRATEGIES FOR ENERGY EFFICIENCY IN WIRELESSSENSOR NETWORKS 709 / Fla' via C. Delicato and Paulo F. Pires -- 25.1 Introduction 709 -- 25.2 Data Link Layer 711 -- 25.2.1 Topology Control Protocols 712 -- 25.2.2 Energy-Efficient MAC Protocols 714 -- 25.2.2.1 Scheduled MAC protocols in WSNs 716 -- 25.2.2.2 Contention-based MAC protocols 717 -- 25.3 Network Layer 719 -- 25.3.1 Flat and Hierarchical Protocols 722 -- 25.4 Transport Layer 725 -- 25.5 Application Layer 729 -- 25.5.1 Task Scheduling 729 -- 25.5.2 Data Aggregation and Data Fusion in WSNs 733 -- 25.5.2.1 Approaches of data fusion for energy efficiency 735 -- 25.5.2.2 Data aggregation strategies 736 -- 25.6 Final Remarks 740 -- References 741 -- 26 ENERGY MANAGEMENT IN HETEROGENEOUS WIRELESS HEALTH CARENETWORKS 751 / Nima Nikzad, Priti Aghera, Piero Zappi, and Tajana S.Rosing. 26.1 Introduction 751 -- 26.2 System Model 753 -- 26.2.1 Health Monitoring Task Model 753 -- 26.3 Collaborative Distributed Environmental Sensing 755 -- 26.3.1 Node Neighborhood and Localization Rate 757 -- 26.3.2 Energy Ratio and Sensing Rate 758 -- 26.3.3 Duty Cycling and Prediction 759 -- 26.4 Task Assignment in a Body Area Network 760 -- 26.4.1 Optimal Task Assignment 760 -- 26.4.2 Dynamic Task Assignment 762 -- 26.4.2.1 DynAGreen algorithm 763 -- 26.4.2.2 DynAGreenLife algorithm 768 -- 26.5 Results 771 -- 26.5.1 Collaborative Sensing 771 -- 26.5.1.1 Results 772 -- 26.5.2 Dynamic Task Assignment 776 -- 26.5.2.1 Performance in static conditions 777 -- 26.5.2.2 Dynamic adaptability 780 -- 26.6 Conclusion 784 -- References 785 -- INDEX 787. |
| Record Nr. | UNINA-9910141439003321 |
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Zomaya Albert Y
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| Hoboken, New Jersey : , : Wiley, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Energy efficient distributed computing systems / / edited by Albert Y. Zomaya, Young Choon Lee
| Energy efficient distributed computing systems / / edited by Albert Y. Zomaya, Young Choon Lee |
| Autore | Zomaya Albert Y |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : Wiley, , c2012 |
| Descrizione fisica | 1 online resource (855 p.) |
| Disciplina | 004/.36 |
| Altri autori (Persone) |
ZomayaAlbert Y
LeeYoung-Choon <1973-> |
| Collana |
Wiley series on parallel and distributed computing
Wiley series on parallel and distributed computing |
| Soggetto topico |
Computer networks - Energy conservation
Electronic data processing - Distributed processing - Energy conservation Green technology |
| ISBN |
1-283-54601-9
9786613858467 1-118-34201-1 1-118-34200-3 1-118-34198-8 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
PREFACE xxix -- ACKNOWLEDGMENTS xxxi -- CONTRIBUTORS xxxiii -- 1 POWER ALLOCATION AND TASK SCHEDULING ON MULTIPROCESSORCOMPUTERS WITH ENERGY AND TIME CONSTRAINTS 1 / Keqin Li -- 1.1 Introduction 1 -- 1.1.1 Energy Consumption 1 -- 1.1.2 Power Reduction 2 -- 1.1.3 Dynamic Power Management 3 -- 1.1.4 Task Scheduling with Energy and Time Constraints 4 -- 1.1.5 Chapter Outline 5 -- 1.2 Preliminaries 5 -- 1.2.1 Power Consumption Model 5 -- 1.2.2 Problem Definitions 6 -- 1.2.3 Task Models 7 -- 1.2.4 Processor Models 8 -- 1.2.5 Scheduling Models 9 -- 1.2.6 Problem Decomposition 9 -- 1.2.7 Types of Algorithms 10 -- 1.3 Problem Analysis 10 -- 1.3.1 Schedule Length Minimization 10 -- 1.3.1.1 Uniprocessor computers 10 -- 1.3.1.2 Multiprocessor computers 11 -- 1.3.2 Energy Consumption Minimization 12 -- 1.3.2.1 Uniprocessor computers 12 -- 1.3.2.2 Multiprocessor computers 13 -- 1.3.3 Strong NP-Hardness 14 -- 1.3.4 Lower Bounds 14 -- 1.3.5 Energy-Delay Trade-off 15 -- 1.4 Pre-Power-Determination Algorithms 16 -- 1.4.1 Overview 16 -- 1.4.2 Performance Measures 17 -- 1.4.3 Equal-Time Algorithms and Analysis 18 -- 1.4.3.1 Schedule length minimization 18 -- 1.4.3.2 Energy consumption minimization 19 -- 1.4.4 Equal-Energy Algorithms and Analysis 19 -- 1.4.4.1 Schedule length minimization 19 -- 1.4.4.2 Energy consumption minimization 21 -- 1.4.5 Equal-Speed Algorithms and Analysis 22 -- 1.4.5.1 Schedule length minimization 22 -- 1.4.5.2 Energy consumption minimization 23 -- 1.4.6 Numerical Data 24 -- 1.4.7 Simulation Results 25 -- 1.5 Post-Power-Determination Algorithms 28 -- 1.5.1 Overview 28 -- 1.5.2 Analysis of List Scheduling Algorithms 29 -- 1.5.2.1 Analysis of algorithm LS 29 -- 1.5.2.2 Analysis of algorithm LRF 30 -- 1.5.3 Application to Schedule Length Minimization 30 -- 1.5.4 Application to Energy Consumption Minimization 31 -- 1.5.5 Numerical Data 32 -- 1.5.6 Simulation Results 32 -- 1.6 Summary and Further Research 33 -- References 34 -- 2 POWER-AWARE HIGH PERFORMANCE COMPUTING 39 / Rong Ge and Kirk W. Cameron.
2.1 Introduction 39 -- 2.2 Background 41 -- 2.2.1 Current Hardware Technology and Power Consumption 41 -- 2.2.1.1 Processor power 41 -- 2.2.1.2 Memory subsystem power 42 -- 2.2.2 Performance 43 -- 2.2.3 Energy Efficiency 44 -- 2.3 Related Work 45 -- 2.3.1 Power Profiling 45 -- 2.3.1.1 Simulator-based power estimation 45 -- 2.3.1.2 Direct measurements 46 -- 2.3.1.3 Event-based estimation 46 -- 2.3.2 Performance Scalability on Power-Aware Systems 46 -- 2.3.3 Adaptive Power Allocation for Energy-Efficient Computing47 -- 2.4 PowerPack: Fine-Grain Energy Profiling of HPC Applications48 -- 2.4.1 Design and Implementation of PowerPack 48 -- 2.4.1.1 Overview 48 -- 2.4.1.2 Fine-grain systematic power measurement 50 -- 2.4.1.3 Automatic power profiling and code synchronization51 -- 2.4.2 Power Profiles of HPC Applications and Systems 53 -- 2.4.2.1 Power distribution over components 53 -- 2.4.2.2 Power dynamics of applications 54 -- 2.4.2.3 Power bounds on HPC systems 55 -- 2.4.2.4 Power versus dynamic voltage and frequency scaling57 -- 2.5 Power-Aware Speedup Model 59 -- 2.5.1 Power-Aware Speedup 59 -- 2.5.1.1 Sequential execution time for a single workload T1(w, f) 60 -- 2.5.1.2 Sequential execution time for an ON-chip/OFF-chipworkload 60 -- 2.5.1.3 Parallel execution time on N processors for anON-/OFF-chip workload with DOP = i 61 -- 2.5.1.4 Power-aware speedup for DOP and ON-/OFF-chip workloads62 -- 2.5.2 Model Parametrization and Validation 63 -- 2.5.2.1 Coarse-grain parametrization and validation 64 -- 2.5.2.2 Fine-grain parametrization and validation 66 -- 2.6 Model Usages 69 -- 2.6.1 Identification of Optimal System Configurations 70 -- 2.6.2 PAS-Directed Energy-Driven Runtime Frequency Scaling71 -- 2.7 Conclusion 73 -- References 75 -- 3 ENERGY EFFICIENCY IN HPC SYSTEMS 81 / Ivan Rodero and Manish Parashar -- 3.1 Introduction 81 -- 3.2 Background and Related Work 83 -- 3.2.1 CPU Power Management 83 -- 3.2.1.1 OS-level CPU power management 83 -- 3.2.1.2 Workload-level CPU power management 84. 3.2.1.3 Cluster-level CPU power management 84 -- 3.2.2 Component-Based Power Management 85 -- 3.2.2.1 Memory subsystem 85 -- 3.2.2.2 Storage subsystem 86 -- 3.2.3 Thermal-Conscious Power Management 87 -- 3.2.4 Power Management in Virtualized Datacenters 87 -- 3.3 Proactive, Component-Based Power Management 88 -- 3.3.1 Job Allocation Policies 88 -- 3.3.2 Workload Profiling 90 -- 3.4 Quantifying Energy Saving Possibilities 91 -- 3.4.1 Methodology 92 -- 3.4.2 Component-Level Power Requirements 92 -- 3.4.3 Energy Savings 94 -- 3.5 Evaluation of the Proposed Strategies 95 -- 3.5.1 Methodology 96 -- 3.5.2 Workloads 96 -- 3.5.3 Metrics 97 -- 3.6 Results 97 -- 3.7 Concluding Remarks 102 -- 3.8 Summary 103 -- References 104 -- 4 A STOCHASTIC FRAMEWORK FOR HIERARCHICAL SYSTEM-LEVEL POWERMANAGEMENT 109 / Peng Rong and Massoud Pedram -- 4.1 Introduction 109 -- 4.2 Related Work 111 -- 4.3 A Hierarchical DPM Architecture 113 -- 4.4 Modeling 114 -- 4.4.1 Model of the Application Pool 114 -- 4.4.2 Model of the Service Flow Control 118 -- 4.4.3 Model of the Simulated Service Provider 119 -- 4.4.4 Modeling Dependencies between SPs 120 -- 4.5 Policy Optimization 122 -- 4.5.1 Mathematical Formulation 122 -- 4.5.2 Optimal Time-Out Policy for Local Power Manager 123 -- 4.6 Experimental Results 125 -- 4.7 Conclusion 130 -- References 130 -- 5 ENERGY-EFFICIENT RESERVATION INFRASTRUCTURE FOR GRIDS,CLOUDS, AND NETWORKS 133 / Anne-Ce' cile Orgerie and Laurent Lefe` vre -- 5.1 Introduction 133 -- 5.2 Related Works 134 -- 5.2.1 Server and Data Center Power Management 135 -- 5.2.2 Node Optimizations 135 -- 5.2.3 Virtualization to Improve Energy Efficiency 136 -- 5.2.4 Energy Awareness in Wired Networking Equipment 136 -- 5.2.5 Synthesis 137 -- 5.3 ERIDIS: Energy-Efficient Reservation Infrastructure forLarge-Scale Distributed Systems 138 -- 5.3.1 ERIDIS Architecture 138 -- 5.3.2 Management of the Resource Reservations 141 -- 5.3.3 Resource Management and On/Off Algorithms 145 -- 5.3.4 Energy-Consumption Estimates 146. 5.3.5 Prediction Algorithms 146 -- 5.4 EARI: Energy-Aware Reservation Infrastructure for DataCenters and Grids 147 -- 5.4.1 EARI's Architecture 147 -- 5.4.2 Validation of EARI on Experimental Grid Traces 147 -- 5.5 GOC: Green Open Cloud 149 -- 5.5.1 GOC's Resource Manager Architecture 150 -- 5.5.2 Validation of the GOC Framework 152 -- 5.6 HERMES: High Level Energy-Aware Model for BandwidthReservation in End-To-End Networks 152 -- 5.6.1 HERMES' Architecture 154 -- 5.6.2 The Reservation Process of HERMES 155 -- 5.6.3 Discussion 157 -- 5.7 Summary 158 -- References 158 -- 6 ENERGY-EFFICIENT JOB PLACEMENT ON CLUSTERS, GRIDS, ANDCLOUDS 163 / Damien Borgetto, Henri Casanova, Georges Da Costa, andJean-Marc Pierson -- 6.1 Problem and Motivation 163 -- 6.1.1 Context 163 -- 6.1.2 Chapter Roadmap 164 -- 6.2 Energy-Aware Infrastructures 164 -- 6.2.1 Buildings 165 -- 6.2.2 Context-Aware Buildings 165 -- 6.2.3 Cooling 166 -- 6.3 Current Resource Management Practices 167 -- 6.3.1 Widely Used Resource Management Systems 167 -- 6.3.2 Job Requirement Description 169 -- 6.4 Scientific and Technical Challenges 170 -- 6.4.1 Theoretical Difficulties 170 -- 6.4.2 Technical Difficulties 170 -- 6.4.3 Controlling and Tuning Jobs 171 -- 6.5 Energy-Aware Job Placement Algorithms 172 -- 6.5.1 State of the Art 172 -- 6.5.2 Detailing One Approach 174 -- 6.6 Discussion 180 -- 6.6.1 Open Issues and Opportunities 180 -- 6.6.2 Obstacles for Adoption in Production 182 -- 6.7 Conclusion 183 -- References 184 -- 7 COMPARISON AND ANALYSIS OF GREEDY ENERGY-EFFICIENTSCHEDULING ALGORITHMS FOR COMPUTATIONAL GRIDS 189 / Peder Lindberg, James Leingang, Daniel Lysaker, KashifBilal, Samee Ullah Khan, Pascal Bouvry, Nasir Ghani, NasroMin-Allah, and Juan Li -- 7.1 Introduction 189 -- 7.2 Problem Formulation 191 -- 7.2.1 The System Model 191 -- 7.2.1.1 PEs 191 -- 7.2.1.2 DVS 191 -- 7.2.1.3 Tasks 192 -- 7.2.1.4 Preliminaries 192 -- 7.2.2 Formulating the Energy-Makespan Minimization Problem192 -- 7.3 Proposed Algorithms 193. 7.3.1 Greedy Heuristics 194 -- 7.3.1.1 Greedy heuristic scheduling algorithm 196 -- 7.3.1.2 Greedy-min 197 -- 7.3.1.3 Greedy-deadline 198 -- 7.3.1.4 Greedy-max 198 -- 7.3.1.5 MaxMin 199 -- 7.3.1.6 ObFun 199 -- 7.3.1.7 MinMin StdDev 202 -- 7.3.1.8 MinMax StdDev 202 -- 7.4 Simulations, Results, and Discussion 203 -- 7.4.1 Workload 203 -- 7.4.2 Comparative Results 204 -- 7.4.2.1 Small-size problems 204 -- 7.4.2.2 Large-size problems 206 -- 7.5 Related Works 211 -- 7.6 Conclusion 211 -- References 212 -- 8 TOWARD ENERGY-AWARE SCHEDULING USING MACHINE LEARNING215 / Josep LL. Berral, In igo Goiri, Ramon Nou, FerranJulia` , Josep O. Fito' , Jordi Guitart, Ricard Gavalda', and Jordi Torres -- 8.1 Introduction 215 -- 8.1.1 Energetic Impact of the Cloud 216 -- 8.1.2 An Intelligent Way to Manage Data Centers 216 -- 8.1.3 Current Autonomic Computing Techniques 217 -- 8.1.4 Power-Aware Autonomic Computing 217 -- 8.1.5 State of the Art and Case Study 218 -- 8.2 Intelligent Self-Management 218 -- 8.2.1 Classical AI Approaches 219 -- 8.2.1.1 Heuristic algorithms 219 -- 8.2.1.2 AI planning 219 -- 8.2.1.3 Semantic techniques 219 -- 8.2.1.4 Expert systems and genetic algorithms 220 -- 8.2.2 Machine Learning Approaches 220 -- 8.2.2.1 Instance-based learning 221 -- 8.2.2.2 Reinforcement learning 222 -- 8.2.2.3 Feature and example selection 225 -- 8.3 Introducing Power-Aware Approaches 225 -- 8.3.1 Use of Virtualization 226 -- 8.3.2 Turning On and Off Machines 228 -- 8.3.3 Dynamic Voltage and Frequency Scaling 229 -- 8.3.4 Hybrid Nodes and Data Centers 230 -- 8.4 Experiences of Applying ML on Power-Aware Self-Management230 -- 8.4.1 Case Study Approach 231 -- 8.4.2 Scheduling and Power Trade-Off 231 -- 8.4.3 Experimenting with Power-Aware Techniques 233 -- 8.4.4 Applying Machine Learning 236 -- 8.4.5 Conclusions from the Experiments 238 -- 8.5 Conclusions on Intelligent Power-Aware Self-Management238 -- References 240 -- 9 ENERGY EFFICIENCY METRICS FOR DATA CENTERS 245 / Javid Taheri and Albert Y. Zomaya. 9.1 Introduction 245 -- 9.1.1 Background 245 -- 9.1.2 Data Center Energy Use 246 -- 9.1.3 Data Center Characteristics 246 -- 9.1.3.1 Electric power 247 -- 9.1.3.2 Heat removal 249 -- 9.1.4 Energy Efficiency 250 -- 9.2 Fundamentals of Metrics 250 -- 9.2.1 Demand and Constraints on Data Center Operators 250 -- 9.2.2 Metrics 251 -- 9.2.2.1 Criteria for good metrics 251 -- 9.2.2.2 Methodology 252 -- 9.2.2.3 Stability of metrics 252 -- 9.3 Data Center Energy Efficiency 252 -- 9.3.1 Holistic IT Efficiency Metrics 252 -- 9.3.1.1 Fixed versus proportional overheads 254 -- 9.3.1.2 Power versus energy 254 -- 9.3.1.3 Performance versus productivity 255 -- 9.3.2 Code of Conduct 256 -- 9.3.2.1 Environmental statement 256 -- 9.3.2.2 Problem statement 256 -- 9.3.2.3 Scope of the CoC 257 -- 9.3.2.4 Aims and objectives of CoC 258 -- 9.3.3 Power Use in Data Centers 259 -- 9.3.3.1 Data center IT power to utility power relationship259 -- 9.3.3.2 Chiller efficiency and external temperature 260 -- 9.4 Available Metrics 260 -- 9.4.1 The Green Grid 261 -- 9.4.1.1 Power usage effectiveness (PUE) 261 -- 9.4.1.2 Data center efficiency (DCE) 262 -- 9.4.1.3 Data center infrastructure efficiency (DCiE) 262 -- 9.4.1.4 Data center productivity (DCP) 263 -- 9.4.2 McKinsey 263 -- 9.4.3 Uptime Institute 264 -- 9.4.3.1 Site infrastructure power overhead multiplier (SI-POM)265 -- 9.4.3.2 IT hardware power overhead multiplier (H-POM) 266 -- 9.4.3.3 DC hardware compute load per unit of computing work done266 -- 9.4.3.4 Deployed hardware utilization ratio (DH-UR) 266 -- 9.4.3.5 Deployed hardware utilization efficiency (DH-UE) 267 -- 9.5 Harmonizing Global Metrics for Data Center Energy Efficiency267 -- References 268 -- 10 AUTONOMIC GREEN COMPUTING IN LARGE-SCALE DATA CENTERS271 / Haoting Luo, Bithika Khargharia, Salim Hariri, and YoussifAl-Nashif / -- 10.1 Introduction 271 -- 10.2 Related Technologies and Techniques 272 -- 10.2.1 Power Optimization Techniques in Data Centers 272 -- 10.2.2 Design Model 273. 10.2.3 Networks 274 -- 10.2.4 Data Center Power Distribution 275 -- 10.2.5 Data Center Power-Efficient Metrics 276 -- 10.2.6 Modeling Prototype and Testbed 277 -- 10.2.7 Green Computing 278 -- 10.2.8 Energy Proportional Computing 280 -- 10.2.9 Hardware Virtualization Technology 281 -- 10.2.10 Autonomic Computing 282 -- 10.3 Autonomic Green Computing: A Case Study 283 -- 10.3.1 Autonomic Management Platform 285 -- 10.3.1.1 Platform architecture 285 -- 10.3.1.2 DEVS-based modeling and simulation platform 285 -- 10.3.1.3 Workload generator 287 -- 10.3.2 Model Parameter Evaluation 288 -- 10.3.2.1 State transitioning overhead 288 -- 10.3.2.2 VM template evaluation 289 -- 10.3.2.3 Scalability analysis 291 -- 10.3.3 Autonomic Power Efficiency Management Algorithm(Performance Per Watt) 291 -- 10.3.4 Simulation Results and Evaluation 293 -- 10.3.4.1 Analysis of energy and performance trade-offs 296 -- 10.4 Conclusion and Future Directions 297 -- References 298 -- 11 ENERGY AND THERMAL AWARE SCHEDULING IN DATA CENTERS301 / Gaurav Dhiman, Raid Ayoub, and Tajana S. Rosing -- 11.1 Introduction 301 -- 11.2 Related Work 302 -- 11.3 Intermachine Scheduling 305 -- 11.3.1 Performance and Power Profile of VMs 305 -- 11.3.2 Architecture 309 -- 11.3.2.1 vgnode 309 -- 11.3.2.2 vgxen 310 -- 11.3.2.3 vgdom 312 -- 11.3.2.4 vgserv 312 -- 11.4 Intramachine Scheduling 315 -- 11.4.1 Air-Forced Thermal Modeling and Cost 316 -- 11.4.2 Cooling Aware Dynamic Workload Scheduling 317 -- 11.4.3 Scheduling Mechanism 318 -- 11.4.4 Cooling Costs Predictor 319 -- 11.5 Evaluation 321 -- 11.5.1 Intermachine Scheduler (vGreen) 321 -- 11.5.2 Heterogeneous Workloads 323 -- 11.5.2.1 Comparison with DVFS policies 325 -- 11.5.2.2 Homogeneous workloads 328 -- 11.5.3 Intramachine Scheduler (Cool and Save) 328 -- 11.5.3.1 Results 331 -- 11.5.3.2 Overhead of CAS 333 -- 11.6 Conclusion 333 -- References 334 -- 12 QOS-AWARE POWER MANAGEMENT IN DATA CENTERS 339 / Jiayu Gong and Cheng-Zhong Xu -- 12.1 Introduction 339. 12.2 Problem Classification 340 -- 12.2.1 Objective and Constraint 340 -- 12.2.2 Scope and Time Granularities 340 -- 12.2.3 Methodology 341 -- 12.2.4 Power Management Mechanism 342 -- 12.3 Energy Efficiency 344 -- 12.3.1 Energy-Efficiency Metrics 344 -- 12.3.2 Improving Energy Efficiency 346 -- 12.3.2.1 Energy minimization with performance guarantee 346 -- 12.3.2.2 Performance maximization under power budget 348 -- 12.3.2.3 Trade-off between power and performance 348 -- 12.3.3 Energy-Proportional Computing 350 -- 12.4 Power Capping 351 -- 12.5 Conclusion 353 -- References 356 -- 13 ENERGY-EFFICIENT STORAGE SYSTEMS FOR DATA CENTERS361 / Sudhanva Gurumurthi and Anand Sivasubramaniam -- 13.1 Introduction 361 -- 13.2 Disk Drive Operation and Disk Power 362 -- 13.2.1 An Overview of Disk Drives 362 -- 13.2.2 Sources of Disk Power Consumption 363 -- 13.2.3 Disk Activity and Power Consumption 365 -- 13.3 Disk and Storage Power Reduction Techniques 366 -- 13.3.1 Exploiting the STANDBY State 368 -- 13.3.2 Reducing Seek Activity 369 -- 13.3.3 Achieving Energy Proportionality 369 -- 13.3.3.1 Hardware approaches 369 -- 13.3.3.2 Software approaches 370 -- 13.4 Using Nonvolatile Memory and Solid-State Disks 371 -- 13.5 Conclusions 372 -- References 373 -- 14 AUTONOMIC ENERGY/PERFORMANCE OPTIMIZATIONS FOR MEMORY INSERVERS 377 / Bithika Khargharia and Mazin Yousif -- 14.1 Introduction 378 -- 14.2 Classifications of Dynamic Power Management Techniques380 -- 14.2.1 Heuristic and Predictive Techniques 380 -- 14.2.2 QoS and Energy Trade-Offs 381 -- 14.3 Applications of Dynamic Power Management (DPM) 382 -- 14.3.1 Power Management of System Components in Isolation382 -- 14.3.2 Joint Power Management of System Components 383 -- 14.3.3 Holistic System-Level Power Management 383 -- 14.4 Autonomic Power and Performance Optimization of MemorySubsystems in Server Platforms 384 -- 14.4.1 Adaptive Memory Interleaving Technique for Power andPerformance Management 384 -- 14.4.1.1 Formulating the optimization problem 386. 14.4.1.2 Memory appflow 389 -- 14.4.2 Industry Techniques 389 -- 14.4.2.1 Enhancements in memory hardware design 390 -- 14.4.2.2 Adding more operating states 390 -- 14.4.2.3 Faster transition to and from low power states 390 -- 14.4.2.4 Memory consolidation 390 -- 14.5 Conclusion 391 -- References 391 -- 15 ROD: A PRACTICAL APPROACH TO IMPROVING RELIABILITY OFENERGY-EFFICIENT PARALLEL DISK SYSTEMS 395 / Shu Yin, Xiaojun Ruan, Adam Manzanares, and XiaoQin -- 15.1 Introduction 395 -- 15.2 Modeling Reliability of Energy-Efficient Parallel Disks396 -- 15.2.1 The MINT Model 396 -- 15.2.1.1 Disk utilization 398 -- 15.2.1.2 Temperature 398 -- 15.2.1.3 Power-state transition frequency 399 -- 15.2.1.4 Single disk reliability model 399 -- 15.2.2 MAID, Massive Arrays of Idle Disks 400 -- 15.3 Improving Reliability of MAID via Disk Swapping 401 -- 15.3.1 Improving Reliability of Cache Disks in MAID 401 -- 15.3.2 Swapping Disks Multiple Times 404 -- 15.4 Experimental Results and Evaluation 405 -- 15.4.1 Experimental Setup 405 -- 15.4.2 Disk Utilization 406 -- 15.4.3 The Single Disk Swapping Strategy 406 -- 15.4.4 The Multiple Disk Swapping Strategy 409 -- 15.5 Related Work 411 -- 15.6 Conclusions 412 -- References 413 -- 16 EMBRACING THE MEMORY AND I/O WALLS FOR ENERGY-EFFICIENTSCIENTIFIC COMPUTING 417 / Chung-Hsing Hsu and Wu-Chun Feng -- 16.1 Introduction 417 -- 16.2 Background and Related Work 420 -- 16.2.1 DVFS-Enabled Processors 420 -- 16.2.2 DVFS Scheduling Algorithms 421 -- 16.2.3 Memory-Aware, Interval-Based Algorithms 422 -- 16.3 β-Adaptation: A New DVFS Algorithm 423 -- 16.3.1 The Compute-Boundedness Metric, β 423 -- 16.3.2 The Frequency Calculating Formula, f ∗ 424 -- 16.3.3 The Online β Estimation 425 -- 16.3.4 Putting It All Together 427 -- 16.4 Algorithm Effectiveness 429 -- 16.4.1 A Comparison to Other DVFS Algorithms 429 -- 16.4.2 Frequency Emulation 432 -- 16.4.3 The Minimum Dependence to the PMU 436 -- 16.5 Conclusions and Future Work 438 -- References 439. 17 MULTIPLE FREQUENCY SELECTION IN DVFS-ENABLED PROCESSORS TOMINIMIZE ENERGY CONSUMPTION 443 / Nikzad Babaii Rizvandi, Albert Y. Zomaya, Young Choon Lee,Ali Javadzadeh Boloori, and Javid Taheri -- 17.1 Introduction 443 -- 17.2 Energy Efficiency in HPC Systems 444 -- 17.3 Exploitation of Dynamic Voltage-Frequency Scaling446 -- 17.3.1 Independent Slack Reclamation 446 -- 17.3.2 Integrated Schedule Generation 447 -- 17.4 Preliminaries 448 -- 17.4.1 System and Application Models 448 -- 17.4.2 Energy Model 448 -- 17.5 Energy-Aware Scheduling via DVFS 450 -- 17.5.1 Optimum Continuous Frequency 450 -- 17.5.2 Reference Dynamic Voltage-Frequency Scaling (RDVFS)451 -- 17.5.3 Maximum-Minimum-Frequency for DynamicVoltage-Frequency Scaling (MMF-DVFS) 452 -- 17.5.4 Multiple Frequency Selection for DynamicVoltage-Frequency Scaling (MFS-DVFS) 453 -- 17.5.4.1 Task eligibility 454 -- 17.6 Experimental Results 456 -- 17.6.1 Simulation Settings 456 -- 17.6.2 Results 458 -- 17.7 Conclusion 461 -- References 461 -- 18 THE PARAMOUNTCY OF RECONFIGURABLE COMPUTING 465 / Reiner Hartenstein -- 18.1 Introduction 465 -- 18.2 Why Computers are Important 466 -- 18.2.1 Computing for a Sustainable Environment 470 -- 18.3 Performance Progress Stalled 472 -- 18.3.1 Unaffordable Energy Consumption of Computing 473 -- 18.3.2 Crashing into the Programming Wall 475 -- 18.4 The Tail is Wagging the Dog (Accelerators) 488 -- 18.4.1 Hardwired Accelerators 489 -- 18.4.2 Programmable Accelerators 490 -- 18.5 Reconfigurable Computing 494 -- 18.5.1 Speedup Factors by FPGAs 498 -- 18.5.2 The Reconfigurable Computing Paradox 501 -- 18.5.3 Saving Energy by Reconfigurable Computing 505 -- 18.5.3.1 Traditional green computing 506 -- 18.5.3.2 The role of graphics processors 507 -- 18.5.3.3 Wintel versus ARM 508 -- 18.5.4 Reconfigurable Computing is the Silver Bullet 511 -- 18.5.4.1 A new world model of computing 511 -- 18.5.5 The Twin-Paradigm Approach to Tear Down the Wall 514 -- 18.5.6 A Mass Movement Needed as Soon as Possible 517. 18.5.6.1 Legacy software from the mainframe age 518 -- 18.5.7 How to Reinvent Computing 519 18.6 Conclusions 526 -- References 529 -- 19 WORKLOAD CLUSTERING FOR INCREASING ENERGY SAVINGS ONEMBEDDED MPSOCS 549 / Ozcan Ozturk, Mahmut Kandemir, and Sri Hari KrishnaNarayanan -- 19.1 Introduction 549 -- 19.2 Embedded MPSoC Architecture, Execution Model, and RelatedWork 550 -- 19.3 Our Approach 551 -- 19.3.1 Overview 551 -- 19.3.2 Technical Details and Problem Formulation 553 -- 19.3.2.1 System and job model 553 -- 19.3.2.2 Mathematical programing model 554 -- 19.3.2.3 Example 557 -- 19.4 Experimental Evaluation 560 -- 19.5 Conclusions 564 -- References 565 -- 20 ENERGY-EFFICIENT INTERNET INFRASTRUCTURE 567 / Weirong Jiang and Viktor K. Prasanna -- 20.1 Introduction 567 -- 20.1.1 Performance Challenges 568 -- 20.1.2 Existing Packet Forwarding Approaches 570 -- 20.1.2.1 Software approaches 570 -- 20.1.2.2 Hardware approaches 571 -- 20.2 SRAM-Based Pipelined IP Lookup Architectures: Alternativeto TCAMs 571 -- 20.3 Data Structure Optimization for Power Efficiency 573 -- 20.3.1 Problem Formulation 574 -- 20.3.1.1 Non-pipelined and pipelined engines 574 -- 20.3.1.2 Power function of SRAM 575 -- 20.3.2 Special Case: Uniform Stride 576 -- 20.3.3 Dynamic Programming 576 -- 20.3.4 Performance Evaluation 577 -- 20.3.4.1 Results for non-pipelined architecture 578 -- 20.3.4.2 Results for pipelined architecture 578 -- 20.4 Architectural Optimization to Reduce Dynamic PowerDissipation 580 -- 20.4.1 Analysis and Motivation 581 -- 20.4.1.1 Traffic locality 582 -- 20.4.1.2 Traffic rate variation 582 -- 20.4.1.3 Access frequency on different stages 583 -- 20.4.2 Architecture-Specific Techniques 583 -- 20.4.2.1 Inherent caching 584 -- 20.4.2.2 Local clocking 584 -- 20.4.2.3 Fine-grained memory enabling 585 -- 20.4.3 Performance Evaluation 585 -- 20.5 Related Work 588 -- 20.6 Summary 589 -- References 589 -- 21 DEMAND RESPONSE IN THE SMART GRID: A DISTRIBUTED COMPUTINGPERSPECTIVE 593 / Chen Wang and Martin De Groot. 21.1 Introduction 593 -- 21.2 Demand Response 595 -- 21.2.1 Existing Demand Response Programs 595 -- 21.2.2 Demand Response Supported by the Smart Grid 597 -- 21.3 Demand Response as a Distributed System 600 -- 21.3.1 An Overlay Network for Demand Response 600 -- 21.3.2 Event Driven Demand Response 602 -- 21.3.3 Cost Driven Demand Response 604 -- 21.3.4 A Decentralized Demand Response Framework 609 -- 21.3.5 Accountability of Coordination Decision Making 610 -- 21.4 Summary 611 -- References 611 -- 22 RESOURCE MANAGEMENT FOR DISTRIBUTED MOBILE COMPUTING615 / Jong-Kook Kim -- 22.1 Introduction 615 -- 22.2 Single-Hop Energy-Constrained Environment 617 -- 22.2.1 System Model 617 -- 22.2.2 Related Work 620 -- 22.2.3 Heuristic Descriptions 621 -- 22.2.3.1 Mapping event 621 -- 22.2.3.2 Scheduling communications 621 -- 22.2.3.3 Opportunistic load balancing and minimum energy greedyheuristics 622 -- 22.2.3.4 ME-MC heuristic 622 -- 22.2.3.5 ME-ME heuristic 624 -- 22.2.3.6 CRME heuristic 625 -- 22.2.3.7 Originator and random 626 -- 22.2.3.8 Upper bound 626 -- 22.2.4 Simulation Model 628 -- 22.2.5 Results 630 -- 22.2.6 Summary 634 -- 22.3 Multihop Distributed Mobile Computing Environment 635 -- 22.3.1 The Multihop System Model 635 -- 22.3.2 Energy-Aware Routing Protocol 636 -- 22.3.2.1 Overview 636 -- 22.3.2.2 DSDV 637 -- 22.3.2.3 DSDV remaining energy 637 -- 22.3.2.4 DSDV-energy consumption per remaining energy 637 -- 22.3.3 Heuristic Description 638 -- 22.3.3.1 Random 638 -- 22.3.3.2 Estimated minimum total energy (EMTE) 638 -- 22.3.3.3 K-percent-speed (KPS) and K-percent-energy (KPE)639 -- 22.3.3.4 Energy ratio and distance (ERD) 639 -- 22.3.3.5 ETC and distance (ETCD) 640 -- 22.3.3.6 Minimum execution time (MET) 640 -- 22.3.3.7 Minimum completion time (MCT) and minimum completiontime with DVS (MCT-DVS) 640 -- 22.3.3.8 Switching algorithm (SA) 640 -- 22.3.4 Simulation Model 641 -- 22.3.5 Results 643 -- 22.3.5.1 Distributed resource management 643 -- 22.3.5.2 Energy-aware protocol 644. 22.3.6 Summary 644 -- 22.4 Future Work 647 -- References 647 -- 23 AN ENERGY-AWARE FRAMEWORK FOR MOBILE DATA MINING 653 / Carmela Comito, Domenico Talia, and Paolo Trunfio -- 23.1 Introduction 653 -- 23.2 System Architecture 654 -- 23.3 Mobile Device Components 657 -- 23.4 Energy Model 659 -- 23.5 Clustering Scheme 664 -- 23.5.1 Clustering the M2M Architecture 666 -- 23.6 Conclusion 670 -- References 670 -- 24 ENERGY AWARENESS AND EFFICIENCY IN WIRELESS SENSORNETWORKS: FROM PHYSICAL DEVICES TO THE COMMUNICATION LINK 673 / Fla' via C. Delicato and Paulo F. Pires -- 24.1 Introduction 673 -- 24.2 WSN and Power Dissipation Models 676 -- 24.2.1 Network and Node Architecture 676 -- 24.2.2 Sources of Power Dissipation in WSNs 679 -- 24.3 Strategies for Energy Optimization 683 -- 24.3.1 Intranode Level 684 -- 24.3.1.1 Duty cycling 685 -- 24.3.1.2 Adaptive sensing 691 -- 24.3.1.3 Dynamic voltage scale (DVS) 693 -- 24.3.1.4 OS task scheduling 694 -- 24.3.2 Internode Level 695 -- 24.3.2.1 Transmission power control 695 -- 24.3.2.2 Dynamic modulation scaling 696 -- 24.3.2.3 Link layer optimizations 698 -- 24.4 Final Remarks 701 -- References 702 -- 25 NETWORK-WIDE STRATEGIES FOR ENERGY EFFICIENCY IN WIRELESSSENSOR NETWORKS 709 / Fla' via C. Delicato and Paulo F. Pires -- 25.1 Introduction 709 -- 25.2 Data Link Layer 711 -- 25.2.1 Topology Control Protocols 712 -- 25.2.2 Energy-Efficient MAC Protocols 714 -- 25.2.2.1 Scheduled MAC protocols in WSNs 716 -- 25.2.2.2 Contention-based MAC protocols 717 -- 25.3 Network Layer 719 -- 25.3.1 Flat and Hierarchical Protocols 722 -- 25.4 Transport Layer 725 -- 25.5 Application Layer 729 -- 25.5.1 Task Scheduling 729 -- 25.5.2 Data Aggregation and Data Fusion in WSNs 733 -- 25.5.2.1 Approaches of data fusion for energy efficiency 735 -- 25.5.2.2 Data aggregation strategies 736 -- 25.6 Final Remarks 740 -- References 741 -- 26 ENERGY MANAGEMENT IN HETEROGENEOUS WIRELESS HEALTH CARENETWORKS 751 / Nima Nikzad, Priti Aghera, Piero Zappi, and Tajana S.Rosing. 26.1 Introduction 751 -- 26.2 System Model 753 -- 26.2.1 Health Monitoring Task Model 753 -- 26.3 Collaborative Distributed Environmental Sensing 755 -- 26.3.1 Node Neighborhood and Localization Rate 757 -- 26.3.2 Energy Ratio and Sensing Rate 758 -- 26.3.3 Duty Cycling and Prediction 759 -- 26.4 Task Assignment in a Body Area Network 760 -- 26.4.1 Optimal Task Assignment 760 -- 26.4.2 Dynamic Task Assignment 762 -- 26.4.2.1 DynAGreen algorithm 763 -- 26.4.2.2 DynAGreenLife algorithm 768 -- 26.5 Results 771 -- 26.5.1 Collaborative Sensing 771 -- 26.5.1.1 Results 772 -- 26.5.2 Dynamic Task Assignment 776 -- 26.5.2.1 Performance in static conditions 777 -- 26.5.2.2 Dynamic adaptability 780 -- 26.6 Conclusion 784 -- References 785 -- INDEX 787. |
| Record Nr. | UNINA-9910808404403321 |
|
Zomaya Albert Y
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| Hoboken, New Jersey : , : Wiley, , c2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Network coding [[electronic resource] /] / edited by Khaldoun Al Agha
| Network coding [[electronic resource] /] / edited by Khaldoun Al Agha |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina | 004.6 |
| Altri autori (Persone) | Al AghaKhaldoun |
| Collana | ISTE |
| Soggetto topico |
Coding theory
Data transmission systems Computer networks - Mathematical models |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-118-56281-X
1-299-18862-1 1-118-56278-X 1-118-56310-7 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Table of Contents; Chapter 1. Network Coding: From Theory to Practice; 1.1. Introduction; 1.2. Theoretical approach; 1.2.1. Max-flow min-cut; 1.2.2. Admissible code; 1.2.3. Linear code; 1.2.4. Algebraic resolution; 1.2.5. Random code; 1.3. Practical approach; 1.3.1. Topologies; 1.3.1.1. Multihop wireless networks; 1.3.1.2. Cellular networks; 1.3.2. Applications; 1.3.2.1. Network coding and TCP; 1.3.2.2. Network coding and P2P; 1.3.2.3. Network coding with priority; 1.4. Conclusion; 1.5. Bibliography; Chapter 2. Fountain Codes and Network Coding for WSNs
2.1. Introduction2.2. Fountain codes; 2.2.1. Generalities; 2.2.2. Families of fountain codes; 2.2.2.1. Random fountain codes; 2.2.2.2. Luby Transform (LT); 2.2.2.3. Raptor code; 2.2.2.4. Code complexity; 2.3. Fountain codes in WSNs; 2.3.1. Implementation; 2.3.2. Protocol of reliability enhancement: ARQs versus fountain codes; 2.3.3. Discharge and overflow; 2.4. Fountain codes and network code for sensor networks; 2.4.1. Impact of network coding on the degree distribution of an LT flow; 2.4.1.1. XOR network coding and LT code; 2.4.2. Design a network code for LT code 2.4.2.1. Solutions of network coding2.4.3. Application to multihop sensor networks; 2.4.3.1. Multihop linear networks; 2.4.3.2. Sensor networks; 2.5. Conclusion; 2.6. Bibliography; Chapter 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding; 3.1. Abstract; 3.2. Introduction; 3.3. Diffusion in ad hoc networks; 3.4. Diffusion and network coding; 3.5. Switched code: incorporate erasure codes with network coding; 3.5.1. Definitions; 3.5.2. Coding function of switched code; 3.6. Decoding function of switched code; 3.7. Design and analysis of a new distribution 3.7.1. Analysis of switched distribution3.8. Conclusion; 3.9. Bibliography; Chapter 4. Security by Network Coding; 4.1. Introduction; 4.2. Attack models; 4.2.1. A type-II wiretap network; 4.2.2. A nice but curious attacker; 4.3. Security for a wiretap network; 4.4. Algebraic security criteria; 4.4.1. Note on random linear network coding; 4.4.2. Algebraic security; 4.4.3. The algebraic security criterion; 4.4.4. Algorithmic application of the criterion; 4.5. Conclusion; 4.6. Bibliography; Chapter 5. Security for Network Coding; 5.1. Introduction; 5.2. Attack models; 5.2.1. Eavesdroppers 5.2.1.1. Internal eavesdroppers5.2.1.2. External eavesdroppers; 5.2.2. Active attackers; 5.2.2.1. Pollution attacks; 5.2.2.2. Flooding attack; 5.2.3. Definition of homomorphic ciphering schemes; 5.2.3.1. Two specific schemes; 5.2.3.2. Completely homomorphic encryption schemes; 5.2.4. Homomorphic encryption and confidentiality in network coding; 5.2.4.1. The case of network coding using XOR; 5.2.4.2. The case of network coding in general; 5.3. Confidentiality; 5.3.1. Alternatives for confidentiality; 5.4. Integrity and authenticity solutions 5.4.1. Definitions of homomorphic MAC and homomorphic hash functions |
| Record Nr. | UNINA-9910141498103321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Network coding [[electronic resource] /] / edited by Khaldoun Al Agha
| Network coding [[electronic resource] /] / edited by Khaldoun Al Agha |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina | 004.6 |
| Altri autori (Persone) | Al AghaKhaldoun |
| Collana | ISTE |
| Soggetto topico |
Coding theory
Data transmission systems Computer networks - Mathematical models |
| ISBN |
1-118-56281-X
1-299-18862-1 1-118-56278-X 1-118-56310-7 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Table of Contents; Chapter 1. Network Coding: From Theory to Practice; 1.1. Introduction; 1.2. Theoretical approach; 1.2.1. Max-flow min-cut; 1.2.2. Admissible code; 1.2.3. Linear code; 1.2.4. Algebraic resolution; 1.2.5. Random code; 1.3. Practical approach; 1.3.1. Topologies; 1.3.1.1. Multihop wireless networks; 1.3.1.2. Cellular networks; 1.3.2. Applications; 1.3.2.1. Network coding and TCP; 1.3.2.2. Network coding and P2P; 1.3.2.3. Network coding with priority; 1.4. Conclusion; 1.5. Bibliography; Chapter 2. Fountain Codes and Network Coding for WSNs
2.1. Introduction2.2. Fountain codes; 2.2.1. Generalities; 2.2.2. Families of fountain codes; 2.2.2.1. Random fountain codes; 2.2.2.2. Luby Transform (LT); 2.2.2.3. Raptor code; 2.2.2.4. Code complexity; 2.3. Fountain codes in WSNs; 2.3.1. Implementation; 2.3.2. Protocol of reliability enhancement: ARQs versus fountain codes; 2.3.3. Discharge and overflow; 2.4. Fountain codes and network code for sensor networks; 2.4.1. Impact of network coding on the degree distribution of an LT flow; 2.4.1.1. XOR network coding and LT code; 2.4.2. Design a network code for LT code 2.4.2.1. Solutions of network coding2.4.3. Application to multihop sensor networks; 2.4.3.1. Multihop linear networks; 2.4.3.2. Sensor networks; 2.5. Conclusion; 2.6. Bibliography; Chapter 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding; 3.1. Abstract; 3.2. Introduction; 3.3. Diffusion in ad hoc networks; 3.4. Diffusion and network coding; 3.5. Switched code: incorporate erasure codes with network coding; 3.5.1. Definitions; 3.5.2. Coding function of switched code; 3.6. Decoding function of switched code; 3.7. Design and analysis of a new distribution 3.7.1. Analysis of switched distribution3.8. Conclusion; 3.9. Bibliography; Chapter 4. Security by Network Coding; 4.1. Introduction; 4.2. Attack models; 4.2.1. A type-II wiretap network; 4.2.2. A nice but curious attacker; 4.3. Security for a wiretap network; 4.4. Algebraic security criteria; 4.4.1. Note on random linear network coding; 4.4.2. Algebraic security; 4.4.3. The algebraic security criterion; 4.4.4. Algorithmic application of the criterion; 4.5. Conclusion; 4.6. Bibliography; Chapter 5. Security for Network Coding; 5.1. Introduction; 5.2. Attack models; 5.2.1. Eavesdroppers 5.2.1.1. Internal eavesdroppers5.2.1.2. External eavesdroppers; 5.2.2. Active attackers; 5.2.2.1. Pollution attacks; 5.2.2.2. Flooding attack; 5.2.3. Definition of homomorphic ciphering schemes; 5.2.3.1. Two specific schemes; 5.2.3.2. Completely homomorphic encryption schemes; 5.2.4. Homomorphic encryption and confidentiality in network coding; 5.2.4.1. The case of network coding using XOR; 5.2.4.2. The case of network coding in general; 5.3. Confidentiality; 5.3.1. Alternatives for confidentiality; 5.4. Integrity and authenticity solutions 5.4.1. Definitions of homomorphic MAC and homomorphic hash functions |
| Record Nr. | UNINA-9910830906603321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Network coding [[electronic resource] /] / edited by Khaldoun Al Agha
| Network coding [[electronic resource] /] / edited by Khaldoun Al Agha |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (306 p.) |
| Disciplina | 004.6 |
| Altri autori (Persone) | Al AghaKhaldoun |
| Collana | ISTE |
| Soggetto topico |
Coding theory
Data transmission systems Computer networks - Mathematical models |
| ISBN |
1-118-56281-X
1-299-18862-1 1-118-56278-X 1-118-56310-7 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Table of Contents; Chapter 1. Network Coding: From Theory to Practice; 1.1. Introduction; 1.2. Theoretical approach; 1.2.1. Max-flow min-cut; 1.2.2. Admissible code; 1.2.3. Linear code; 1.2.4. Algebraic resolution; 1.2.5. Random code; 1.3. Practical approach; 1.3.1. Topologies; 1.3.1.1. Multihop wireless networks; 1.3.1.2. Cellular networks; 1.3.2. Applications; 1.3.2.1. Network coding and TCP; 1.3.2.2. Network coding and P2P; 1.3.2.3. Network coding with priority; 1.4. Conclusion; 1.5. Bibliography; Chapter 2. Fountain Codes and Network Coding for WSNs
2.1. Introduction2.2. Fountain codes; 2.2.1. Generalities; 2.2.2. Families of fountain codes; 2.2.2.1. Random fountain codes; 2.2.2.2. Luby Transform (LT); 2.2.2.3. Raptor code; 2.2.2.4. Code complexity; 2.3. Fountain codes in WSNs; 2.3.1. Implementation; 2.3.2. Protocol of reliability enhancement: ARQs versus fountain codes; 2.3.3. Discharge and overflow; 2.4. Fountain codes and network code for sensor networks; 2.4.1. Impact of network coding on the degree distribution of an LT flow; 2.4.1.1. XOR network coding and LT code; 2.4.2. Design a network code for LT code 2.4.2.1. Solutions of network coding2.4.3. Application to multihop sensor networks; 2.4.3.1. Multihop linear networks; 2.4.3.2. Sensor networks; 2.5. Conclusion; 2.6. Bibliography; Chapter 3. Switched Code for Ad Hoc Networks: Optimizing the Diffusion by Using Network Coding; 3.1. Abstract; 3.2. Introduction; 3.3. Diffusion in ad hoc networks; 3.4. Diffusion and network coding; 3.5. Switched code: incorporate erasure codes with network coding; 3.5.1. Definitions; 3.5.2. Coding function of switched code; 3.6. Decoding function of switched code; 3.7. Design and analysis of a new distribution 3.7.1. Analysis of switched distribution3.8. Conclusion; 3.9. Bibliography; Chapter 4. Security by Network Coding; 4.1. Introduction; 4.2. Attack models; 4.2.1. A type-II wiretap network; 4.2.2. A nice but curious attacker; 4.3. Security for a wiretap network; 4.4. Algebraic security criteria; 4.4.1. Note on random linear network coding; 4.4.2. Algebraic security; 4.4.3. The algebraic security criterion; 4.4.4. Algorithmic application of the criterion; 4.5. Conclusion; 4.6. Bibliography; Chapter 5. Security for Network Coding; 5.1. Introduction; 5.2. Attack models; 5.2.1. Eavesdroppers 5.2.1.1. Internal eavesdroppers5.2.1.2. External eavesdroppers; 5.2.2. Active attackers; 5.2.2.1. Pollution attacks; 5.2.2.2. Flooding attack; 5.2.3. Definition of homomorphic ciphering schemes; 5.2.3.1. Two specific schemes; 5.2.3.2. Completely homomorphic encryption schemes; 5.2.4. Homomorphic encryption and confidentiality in network coding; 5.2.4.1. The case of network coding using XOR; 5.2.4.2. The case of network coding in general; 5.3. Confidentiality; 5.3.1. Alternatives for confidentiality; 5.4. Integrity and authenticity solutions 5.4.1. Definitions of homomorphic MAC and homomorphic hash functions |
| Record Nr. | UNINA-9910841102603321 |
| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
