10622nam 22004573 450 991086523760332120240604080223.09789819726448(electronic bk.)9789819726431(MiAaPQ)EBC31360220(Au-PeEL)EBL31360220(CKB)32213009700041(EXLCZ)993221300970004120240604d2024 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierResource Management in Distributed Systems1st ed.Singapore :Springer Singapore Pte. Limited,2024.©2024.1 online resource (319 pages)Studies in Big Data Series ;v.151Print version: Mukherjee, Anwesha Resource Management in Distributed Systems Singapore : Springer Singapore Pte. Limited,c2024 9789819726431 Intro -- Preface -- Contents -- About the Editors -- Resource Management in Distributed Computing -- 1 Introduction -- 2 Resource Management in Cloud Computing -- 3 Resource Allocation in Edge Computing -- 4 Resource Allocation in Fog Computing -- 5 Load Balancing in Resource Utilization -- 6 Resource Allocation and Scheduling Algorithms -- 7 Summary -- References -- Cloud Computing Resource Management -- 1 Introduction -- 2 Categories of Cloud Resources -- 2.1 Fast Computation Utility -- 2.2 Storage Utility -- 2.3 Communication Utility -- 2.4 Energy/Power Utility -- 2.5 Security Utility -- 3 Classification of Cloud Resource Management Methods -- 3.1 Energy-Aware Resource Management -- 3.2 SLA-Aware Resource Management -- 3.3 Market-Based Resource Management -- 3.4 Load-Balanced Resource Management -- 3.5 Network Load-Aware Resource Management -- 3.6 Hybrid Cloud Resource Management -- 3.7 MCC Resource Management -- 4 Mathematical Model of Performance Evaluation Parameters -- 4.1 Throughput -- 4.2 Network Overhead -- 4.3 VM Migration Time -- 4.4 Number of VM Migrations -- 4.5 Resource Utilization -- 4.6 Energy Consumption -- 4.7 Revenue and Profit -- 4.8 SLA Violation -- 5 Resource Management for Edge and Fog Computing -- 5.1 Resource Management in Edge Computing -- 5.2 Resource Management Issues of Fog Computing -- 6 Resource Management Systems and Simulation Tools -- 6.1 Resource Management Systems in Practice -- 6.2 Simulators for Resource Management -- 7 Research Challenges -- 7.1 Consumer-Based Service Management -- 7.2 Autonomic Resource Management -- 7.3 Resource Information Management -- 7.4 Heterogeneous Resources -- 7.5 Sharing of Network Resources -- 7.6 Security -- 7.7 Large-Scale Cloud Management -- 7.8 Computational Risk Analysis and Management -- 7.9 Multi-parametric Performance Evaluation -- 7.10 Service Benchmarking.7.11 Robustness -- 8 Summary -- References -- Resource Allocation and Placement in Multi-access Edge Computing -- 1 Introduction -- 2 MEC Scenarios and Optimization Objectives -- 3 Resource Allocation -- 3.1 Computing -- 3.2 Communication -- 3.3 Storage -- 3.4 Multi-resource Allocation -- 3.5 Network Slicing -- 4 Placement Issues -- 4.1 Server Placement -- 4.2 Service Placement -- 4.3 NFV Placement -- 5 Challenges and Future Research Directions -- 6 Conclusions -- References -- Resource Scheduling in Integrated IoT and Fog Computing Environments: A Taxonomy, Survey and Future Directions -- 1 Introduction -- 2 Challenges in Cloud Computing -- 3 Fog Computing Architecture -- 3.1 IoT Layer -- 3.2 Fog Layer -- 3.3 Cloud Layer -- 4 Taxonomy of Recent Resource Scheduling Techniques -- 4.1 Static Scheduling -- 4.2 Dynamic Scheduling -- 4.3 Artificial Intelligence -- 5 Federated Learning for QoS Optimisation -- 6 Open Issues and Future Research Directions -- 7 Conclusions -- References -- Trusted Task Offloading and Resource Allocation Strategy in MEC Environment -- 1 Introduction -- 2 Mobile Edge Computing -- 2.1 Cloud Computing -- 2.2 Mobile Edge Cloud Computing -- 3 Trust Evaluation Mechanism -- 3.1 Trusted Identity -- 3.2 Trusted Behavior -- 3.3 Trusted Capability -- 4 Resource Allocation Strategy -- 4.1 Markov Decision Process -- 4.2 Deep Reinforcement Learning -- 4.3 Federated Learning -- 5 Balanced Multi-objective Approach -- 5.1 Entropy -- 5.2 Principal Component Analysis -- 5.3 Kernel Principal Component Analysis -- 6 Experiment -- 6.1 Experiment Setup -- 6.2 Target Weight Coefficient -- 6.3 Task Scaling -- 6.4 Compute Node Expansion -- 7 Conclusion -- References -- Resource Management in Edge Clouds: Latency-Aware Approaches for Big Data Analysis -- 1 Introduction -- 2 Fog, Edge, and Dew Computing.3 Why Latency is Important in Emerging Cloud-Based Services? -- 4 Resource Management in Fog/Edge-Cloud Architecture -- 5 Resource Management Approaches -- 5.1 Application Placement -- 5.2 Load Balancing -- 5.3 Resource Scheduling -- 5.4 Offloading and Caching -- 5.5 Virtual Machine (VM) Migration and Service Function Chaining (SFC) -- 6 Role of SDN in Latency-Aware Resource Management -- 7 AI-Powered Latency-Aware Resource Management -- 8 Conclusion and Future Works -- References -- FSRmSTS-An Optimize Task Scheduling with a Hybrid Approach: Integrating FCFS, SJF, and RR with Median Standard Time Slice -- 1 Introduction -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Analyzing Median -- 4 Experimental Results and Analysis -- 4.1 Illustrative Example 1 -- 4.2 Ready Queue Using FCFS -- 4.3 Ready Queue Using SJF -- 4.4 Ready Queue Using RR -- 4.5 Illustrative Example 2 -- 5 Performance Analysis -- 5.1 Comparison-Performance Metric: Turn Around Time -- 6 Conclusion -- References -- Container Orchestration in Heterogeneous Edge Computing Environments -- 1 Introduction -- 2 Background and Related Works -- 2.1 Kubernetes -- 2.2 Autoscaling -- 2.3 Related Works -- 3 System Design -- 3.1 Replica Placement -- 3.2 Autoscaling Replicas -- 4 Evaluation Environment and Experimental Setup -- 4.1 Testbed Configuration -- 4.2 Benchmark Applications and Workloads -- 4.3 Metrics -- 5 Results and Analysis -- 5.1 Web Service Results -- 5.2 Object Detection Service Results -- 6 Discussions and Limitations -- 7 Conclusions and Future Work -- References -- Resource Targeted Cybersecurity Attacks in Cloud Computing Environments -- 1 Introduction -- 2 Cloud Targeted Attacks -- 2.1 Economic Denial of Sustainability (EDoS) Attack -- 2.2 Side Channel Attacks -- 2.3 Session Hijacking Using Cookie Poisoning -- 2.4 Malware Injection Attacks -- 2.5 Gray Cloud Attacks.2.6 Memory Inspection Attacks -- 2.7 Botcloud and Out-Cloud Attacks -- 2.8 Miscellaneous Attacks -- 3 Summary and Guidelines -- 4 Conclusions -- References -- Load Balancing Using Swarm Intelligence in Cloud Environment -- 1 Introduction -- 1.1 Cloud Computing -- 1.2 Cloud Computing Services -- 1.3 Virtualization -- 2 Load Balancing -- 2.1 Static and Dynamic Load Balancing -- 3 Swarm Intelligence Based Techniques for Load Balancing -- 3.1 Ant Colony Optimization -- 3.2 Particle Swarm Optimization -- 3.3 Genetic Algorithm -- 3.4 BAT Algorithm -- 3.5 Grey Wolf Optimization (GWO) -- 3.6 Artificial Bee Colony (ABC) -- 3.7 Whale Optimization Algorithm (WOA) -- 3.8 Social Spider Optimization -- 3.9 Dragonfly Algorithm -- 3.10 Raven Roosting Optimization (RRO) -- 4 Hybrid Algorithm -- 4.1 Performance Matrices for Load Balancing -- 5 Conclusion -- References -- Interoperability and Portability in Big Data Analysis Based Cloud-Fog-Edge-Dew Computing -- 1 Introduction -- 2 Dew, Edge, Fog and Cloud Computing -- 3 Interoperability in Cloud Computing for Big Data Analysis -- 3.1 Interoperability in Fog/Edge/Dew Computing -- 3.2 Strategies, Architecture, and Technologies -- 4 Portability in Cloud Computing -- 4.1 Cloud Portability at IaaS Level -- 4.2 Cloud Portability at PaaS and SaaS Level -- 4.3 Data Portability -- 5 Management and Orchestration for Interoperability and Portability -- 6 Working Groups and Standardization Bodies in Interoperability and Portability -- 6.1 Cloud Standards Coordination (CSC) -- 6.2 National Institute of Standards and Technology (NIST) -- 6.3 The Open Grid Forum (OGF) -- 6.4 Open Cloud Computing Interface (OCCI) -- 6.5 IEEE SA -- 6.6 Cloud Application Management for Platforms (CAMP) -- 6.7 Topology and Orchestration Specification for Cloud Applications (TOSCA) -- 6.8 Cloud Data Management Interface (CDMI).6.9 The Distributed Management Task Force (DMTF) -- 6.10 IEEE Std 2301™ -- 7 Challenges and Future Direction -- References -- Cyber Attack Victim Separation: New Dimensions to Minimize Attack Effects by Resource Management -- 1 Introduction -- 2 Victim Separation Methods: Classification -- 2.1 Moving Target Defense (MTD) -- 2.2 Resource Isolation -- 2.3 Solutions to Co-residency Attack -- 2.4 Victim Service Migration -- 2.5 Demilitarized Zone (DMZ) -- 3 Discussion and Future Directions -- 4 Conclusions -- References -- eBPF and XDP Technologies as Enablers for Ultra-Fast and Programmable Next-Gen Network Infrastructures -- 1 Introduction -- 2 Background: eeBPF and XDP Technical Description -- 2.1 Main Characteristics -- 2.2 eBPF Programs in the XDP: Advantages and Limitations -- 3 Related Work: eeBPF-Based Networking Applications -- 4 Use Case -- 4.1 Functionality Overview -- 4.2 Technical Details -- 4.3 Testbench -- 4.4 Results -- 5 Conclusion -- References -- Deep Reinforcement Learning (DRL)-Based Methods for Serverless Stream Processing Engines: A Vision, Architectural Elements, and Future Directions -- 1 Introduction -- 2 Significance and Innovation of Our Vision -- 3 Architectural Framework -- 4 Research Issues and Envisioned Approaches/Future Directions -- 4.1 DRL-Based Resource Management Agents -- 4.2 Exploration Aids -- 4.3 Policies for Management of Rewards, States, and Enactment -- 4.4 Automated Techniques for Metrics and Configuration Management -- 5 A Case Study-Use Case Scenario: Multi-source Stream Video Analytics System -- 5.1 Use Case Overview -- 5.2 Use Case Implementation -- 5.3 DRL-SSPE Implementation for Video Grayscale -- 5.4 Results Discussion -- 6 Conclusions and Final Remarks -- References.Studies in Big Data SeriesMukherjee Anwesha1742670De Debashis1346723Buyya Rajkumar722089MiAaPQMiAaPQMiAaPQ9910865237603321Resource Management in Distributed Systems4169382UNINA