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Optimization for communications and networks / / Poompat Saengudomlert
| Optimization for communications and networks / / Poompat Saengudomlert |
| Autore | Saengudomlert Poompat |
| Pubbl/distr/stampa | Enfield, N.H. : , : Science Publishers |
| Descrizione fisica | 1 online resource (220 p.) |
| Disciplina | 621.382 |
| Soggetto topico |
Telecommunication systems - Mathematical models
Mathematical optimization System analysis - Mathematics |
| Soggetto genere / forma | Electronic books. |
| ISBN |
0-429-06581-7
1-4398-7656-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Dedication; Preface; Contents; List of Figures; 1. Introduction; 2. Convex Optimization; 3. Linear Optimization; 4. Integer Linear Optimization; Appendix A: Reviews of Related Mathematics; Appendix B: Solutions to Exercise Problems; Appendix C: Octave Commands for Optimization; Bibliography |
| Record Nr. | UNINA-9910464578303321 |
Saengudomlert Poompat
|
||
| Enfield, N.H. : , : Science Publishers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization for communications and networks / / Poompat Saengudomlert
| Optimization for communications and networks / / Poompat Saengudomlert |
| Autore | Saengudomlert Poompat |
| Pubbl/distr/stampa | Enfield, N.H. : , : Science Publishers |
| Descrizione fisica | 1 online resource (220 p.) |
| Disciplina | 621.382 |
| Soggetto topico |
Telecommunication systems - Mathematical models
Mathematical optimization System analysis - Mathematics |
| ISBN |
0-429-06581-7
1-4398-7656-8 |
| Classificazione | COM043000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Front Cover; Dedication; Preface; Contents; List of Figures; 1. Introduction; 2. Convex Optimization; 3. Linear Optimization; 4. Integer Linear Optimization; Appendix A: Reviews of Related Mathematics; Appendix B: Solutions to Exercise Problems; Appendix C: Octave Commands for Optimization; Bibliography |
| Record Nr. | UNINA-9910789335003321 |
|
Saengudomlert Poompat
|
||
| Enfield, N.H. : , : Science Publishers | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Optimization for learning and control / / Anders Hansson and Martin Andersen
| Optimization for learning and control / / Anders Hansson and Martin Andersen |
| Autore | Hansson Anders |
| Pubbl/distr/stampa | Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] |
| Descrizione fisica | 1 online resource (435 pages) |
| Disciplina | 519.3 |
| Soggetto topico |
System analysis - Mathematics
Mathematical optimization Machine learning - Mathematics Signal processing - Mathematics |
| ISBN |
1-119-80918-5
1-119-80914-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Glossary -- Acronyms -- About the Companion Website -- Part I Introductory Part -- Chapter 1 Introduction -- 1.1 Optimization -- 1.2 Unsupervised Learning -- 1.3 Supervised Learning -- 1.4 System Identification -- 1.5 Control -- 1.6 Reinforcement Learning -- 1.7 Outline -- Chapter 2 Linear Algebra -- 2.1 Vectors and Matrices -- 2.2 Linear Maps and Subspaces -- 2.2.1 Four Fundamental Subspaces -- 2.2.2 Square Matrices -- 2.2.3 Affine Sets -- 2.3 Norms -- 2.4 Algorithm Complexity -- 2.5 Matrices with Structure -- 2.5.1 Diagonal Matrices -- 2.5.2 Orthogonal Matrices -- 2.5.3 Triangular Matrices -- 2.5.4 Symmetric and Skew‐Symmetric Matrices -- 2.5.5 Toeplitz and Hankel Matrices -- 2.5.6 Sparse Matrices -- 2.5.7 Band Matrices -- 2.6 Quadratic Forms and Definiteness -- 2.7 Spectral Decomposition -- 2.8 Singular Value Decomposition -- 2.9 Moore-Penrose Pseudoinverse -- 2.10 Systems of Linear Equations -- 2.10.1 Gaussian Elimination -- 2.10.2 Block Elimination -- 2.11 Factorization Methods -- 2.11.1 LU Factorization -- 2.11.2 Cholesky Factorization -- 2.11.3 Indefinite LDL Factorization -- 2.11.4 QR Factorization -- 2.11.5 Sparse Factorizations -- 2.11.6 Block Factorization -- 2.11.7 Positive Semidefinite Block Factorization -- 2.12 Saddle‐Point Systems -- 2.12.1 H Positive Definite -- 2.12.2 H Positive Semidefinite -- 2.13 Vector and Matrix Calculus -- Exercises -- Chapter 3 Probability Theory -- 3.1 Probability Spaces -- 3.1.1 Probability Measure -- 3.1.2 Probability Function -- 3.1.3 Probability Density Function -- 3.2 Conditional Probability -- 3.3 Independence -- 3.4 Random Variables -- 3.4.1 Vector‐Valued Random Variable -- 3.4.2 Marginal Distribution -- 3.4.3 Independence of Random Variables -- 3.4.4 Function of Random Variable -- 3.5 Conditional Distributions.
3.5.1 Conditional Probability Function -- 3.5.2 Conditional Probability Density Function -- 3.6 Expectations -- 3.6.1 Moments -- 3.6.2 Expected Value of Function of Random Variable -- 3.6.3 Covariance -- 3.7 Conditional Expectations -- 3.8 Convergence of Random Variables -- 3.9 Random Processes -- 3.10 Markov Processes -- 3.11 Hidden Markov Models -- 3.12 Gaussian Processes -- Exercises -- Part II Optimization -- Chapter 4 Optimization Theory -- 4.1 Basic Concepts and Terminology -- 4.1.1 Optimization Problems -- 4.1.2 Equivalent Problems -- 4.2 Convex Sets -- 4.2.1 Convexity‐Preserving Operations -- 4.2.1.1 Intersection -- 4.2.1.2 Affine Transformation -- 4.2.1.3 Perspective Transformation -- 4.2.2 Examples of Convex Sets -- 4.2.2.1 Hyperplanes and Halfspaces -- 4.2.2.2 Polyhedral Sets -- 4.2.2.3 Norm Balls and Ellipsoids -- 4.2.2.4 Convex Cones -- 4.2.3 Generalized Inequalities -- 4.3 Convex Functions -- 4.3.1 First‐ and Second‐Order Conditions for Convexity -- 4.3.2 Convexity‐Preserving Operations -- 4.3.2.1 Scaling, Sums, and Integrals -- 4.3.2.2 Pointwise Maximum and Supremum -- 4.3.2.3 Affine Transformation -- 4.3.2.4 Perspective Transformation -- 4.3.2.5 Partial Infimum -- 4.3.2.6 Square of Nonnegative Convex Functions -- 4.3.3 Examples of Convex Functions -- 4.3.3.1 Norms -- 4.3.3.2 Indicator and Support Functions -- 4.3.4 Conjugation -- 4.3.5 Dual Norms -- 4.4 Subdifferentiability -- 4.4.1 Subdifferential Calculus -- 4.4.1.1 Nonnegative Scaling -- 4.4.1.2 Summation -- 4.4.1.3 Affine Transformation -- 4.4.1.4 Pointwise Maximum -- 4.4.1.5 Subgradients of Conjugate Functions -- 4.5 Convex Optimization Problems -- 4.5.1 Optimality Condition -- 4.5.2 Equality Constrained Convex Problems -- 4.6 Duality -- 4.6.1 Lagrangian Duality -- 4.6.2 Lagrange Dual Problem -- 4.6.3 Fenchel Duality -- 4.7 Optimality Conditions. 4.7.1 Convex Optimization Problems -- 4.7.2 Nonconvex Optimization Problems -- Exercises -- Chapter 5 Optimization Problems -- 5.1 Least‐Squares Problems -- 5.2 Quadratic Programs -- 5.3 Conic Optimization -- 5.3.1 Conic Duality -- 5.3.2 Epigraphical Cones -- 5.4 Rank Optimization -- 5.5 Partially Separability -- 5.5.1 Minimization of Partially Separable Functions -- 5.5.2 Principle of Optimality -- 5.6 Multiparametric Optimization -- 5.7 Stochastic Optimization -- Exercises -- Chapter 6 Optimization Methods -- 6.1 Basic Principles -- 6.1.1 Smoothness -- 6.1.2 Descent Methods -- 6.1.3 Line Search Methods -- 6.1.3.1 Backtracking Line Search -- 6.1.3.2 Bisection Method for Wolfe Conditions -- 6.1.4 Surrogation Methods -- 6.1.4.1 Trust‐Region Methods -- 6.1.4.2 Majorization Minimization -- 6.1.5 Convergence of Sequences -- 6.2 Gradient Descent -- 6.2.1 L‐Smooth Functions -- 6.2.2 Smooth and Convex Functions -- 6.2.3 Smooth and Strongly Convex Functions -- 6.3 Newton's Method -- 6.3.1 The Newton Decrement -- 6.3.2 Analysis of Newton's Method -- 6.3.2.1 Affine Invariance -- 6.3.2.2 Pure Newton Phase -- 6.3.2.3 Damped Newton Phase -- 6.3.3 Equality Constrained Minimization -- 6.4 Variable Metric Methods -- 6.4.1 Quasi‐Newton Updates -- 6.4.1.1 The BFGS Update -- 6.4.1.2 The DFP Update -- 6.4.1.3 The SR1 Update -- 6.4.2 The Barzilai-Borwein Method -- 6.5 Proximal Gradient Method -- 6.5.1 Gradient Projection Method -- 6.5.2 Proximal Quasi‐Newton -- 6.5.3 Accelerated Proximal Gradient Method -- 6.6 Sequential Convex Optimization -- 6.7 Methods for Nonlinear Least‐Squares -- 6.7.1 The Levenberg‐Marquardt Algorithm -- 6.7.2 The Variable Projection Method -- 6.8 Stochastic Optimization Methods -- 6.8.1 Smooth Functions -- 6.8.2 Smooth and Strongly Convex Functions -- 6.8.3 Incremental Methods -- 6.8.4 Adaptive Methods -- 6.8.4.1 AdaGrad -- 6.8.4.2 RMSprop. 6.8.4.3 Adam -- 6.9 Coordinate Descent Methods -- 6.10 Interior‐Point Methods -- 6.10.1 Path‐Following Method -- 6.10.2 Generalized Inequalities -- 6.11 Augmented Lagrangian Methods -- 6.11.1 Method of Multipliers -- 6.11.2 Alternating Direction Method of Multipliers -- 6.11.3 Variable Splitting -- Exercises -- Part III Optimal Control -- Chapter 7 Calculus of Variations -- 7.1 Extremum of Functionals -- 7.1.1 Necessary Condition for Extremum -- 7.1.2 Sufficient Condition for Optimality -- 7.1.3 Constrained Problem -- 7.1.4 Du Bois-Reymond Lemma -- 7.1.5 Generalizations -- 7.2 The Pontryagin Maximum Principle -- 7.2.1 Linear Quadratic Control -- 7.2.2 The Riccati Equation -- 7.3 The Euler-Lagrange Equations -- 7.3.1 Beltrami's Identity -- 7.4 Extensions -- 7.5 Numerical Solutions -- 7.5.1 The Gradient Method -- 7.5.2 The Shooting Method -- 7.5.3 The Discretization Method -- 7.5.4 The Multiple Shooting Method -- 7.5.5 The Collocation Method -- Exercises -- Chapter 8 Dynamic Programming -- 8.1 Finite Horizon Optimal Control -- 8.1.1 Standard Optimization Problem -- 8.1.2 Dynamic Programming -- 8.2 Parametric Approximations -- 8.2.1 Fitted‐Value Iteration -- 8.3 Infinite Horizon Optimal Control -- 8.3.1 Bellman Equation -- 8.4 Value Iterations -- 8.5 Policy Iterations -- 8.5.1 Approximation -- 8.6 Linear Programming Formulation -- 8.6.1 Approximations -- 8.7 Model Predictive Control -- 8.7.1 Infinite Horizon Problem -- 8.7.2 Guessing the Value Function -- 8.7.3 Finite Horizon Approximation -- 8.7.4 Receding Horizon Approximation -- 8.8 Explicit MPC -- 8.9 Markov Decision Processes -- 8.9.1 Stochastic Dynamic Programming -- 8.9.2 Infinite Time Horizon -- 8.9.3 Stochastic Bellman Equation -- 8.10 Appendix -- 8.10.1 Stability and Optimality of Infinite Horizon Problem -- 8.10.2 Stability and Optimality of Stochastic Infinite Time Horizon Problem. 8.10.3 Stability of MPC -- Exercises -- Part IV Learning -- Chapter 9 Unsupervised Learning -- 9.1 Chebyshev Bounds -- 9.2 Entropy -- 9.2.1 Categorical Distribution -- 9.2.2 Ising Distribution -- 9.2.3 Normal Distribution -- 9.3 Prediction -- 9.3.1 Conditional Expectation Predictor -- 9.3.2 Affine Predictor -- 9.3.3 Linear Regression -- 9.4 The Viterbi Algorithm -- 9.5 Kalman Filter on Innovation Form -- 9.6 Viterbi Decoder -- 9.7 Graphical Models -- 9.7.1 Ising Distribution -- 9.7.2 Normal Distribution -- 9.7.3 Markov Random Field -- 9.8 Maximum Likelihood Estimation -- 9.8.1 Categorical Distribution -- 9.8.2 Ising Distribution -- 9.8.3 Normal Distribution -- 9.8.4 Generalizations -- 9.9 Relative Entropy and Cross Entropy -- 9.9.1 Gibbs' Inequality -- 9.9.2 Cross Entropy -- 9.10 The Expectation Maximization Algorithm -- 9.11 Mixture Models -- 9.12 Gibbs Sampling -- 9.13 Boltzmann Machine -- 9.14 Principal Component Analysis -- 9.14.1 Solution -- 9.14.2 Relation to Rank‐Constrained Optimization -- 9.15 Mutual Information -- 9.15.1 Channel Model -- 9.15.2 Orthogonal Case -- 9.15.3 Nonorthogonal Case -- 9.15.4 Relationship to PCA -- 9.16 Cluster Analysis -- Exercises -- Chapter 10 Supervised Learning -- 10.1 Linear Regression -- 10.1.1 Least‐Squares Estimation -- 10.1.2 Maximum Likelihood Estimation -- 10.1.3 Maximum a Posteriori Estimation -- 10.2 Regression in Hilbert Spaces -- 10.2.1 Infinite‐Dimensional LS Problem -- 10.2.2 The Kernel Trick -- 10.3 Gaussian Processes -- 10.3.1 Gaussian MAP Estimate -- 10.3.2 The Kernel Trick -- 10.4 Classification -- 10.4.1 Linear Regression -- 10.4.2 Logistic Regression -- 10.5 Support Vector Machines -- 10.5.1 Hebbian Learning -- 10.5.2 Quadratic Programming Formulation -- 10.5.3 Soft Margin Classification -- 10.5.4 The Dual Problem -- 10.5.5 Recovering the Primal Solution -- 10.5.6 The Kernel Trick. 10.6 Restricted Boltzmann Machine. |
| Record Nr. | UNINA-9910830085103321 |
|
Hansson Anders
|
||
| Hoboken, New Jersey : , : John Wiley & Sons, Inc., , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantitative assessments of distributed systems : methodologies and techniques / / edited by Dario Bruneo and Salvatore Distefano ; cover design by Russell Richardson
| Quantitative assessments of distributed systems : methodologies and techniques / / edited by Dario Bruneo and Salvatore Distefano ; cover design by Russell Richardson |
| Pubbl/distr/stampa | Salem, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (398 p.) |
| Disciplina | 004.029 |
| Collana | Performability Engineering Series |
| Soggetto topico |
Computer systems - Evaluation - Mathematics
System analysis - Mathematics Electronic data processing - Distributed processing - Mathematical models Quantitative research |
| ISBN |
1-119-13113-8
1-119-13115-4 1-119-13114-6 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Contents; Preface; PART I VERIFICATION; 1 Modeling and Verification of Distributed Systems Using Markov Decision Processes; 1.1 Introduction; 1.2 Markov Decision Processes; 1.3 Markov Decision Well-Formed Net formalism; 1.4 Case study: Peer-to-Peer Botnets; 1.5 Conclusion; Appendices: Well-formed Net Formalism; A.0.1 Syntax of Basic Predicates; A.0.2 Markings and Enabling; References; 2 Quantitative Analysis of Distributed Systems in Stoklaim: A Tutorial; 2.1 Introduction; 2.2 StoKlaim: Stochastic Klaim; 2.2.1 Klaim in a Nutshell; 2.2.2 Syntactic Categories
2.2.3 StoKlaim Syntax2.2.4 StoKlaim at Work; 2.3 StoKlaim Operational Semantics; 2.3.1 Rate Transition Systems; 2.3.2 StoKlaim: RTS-based Semantics; 2.4 MoSL: Mobile Stochastic Logic; 2.5 jSAM: Java Stochastic Model-Checker; 2.6 Leader Election in StoKlaim; 2.6.1 As far as it can; 2.6.2 Asynchronous Leader Election; 2.7 Concluding Remarks; References; 3 Stochastic Path Properties of Distributed Systems: the CSLTA Approach; 3.1 Introduction; 3.2 The Reference Formalisms for System Definition; 3.3 The Formalism for Path Property Definition: CSLTA; 3.4 CSLTA at work: a Fault-Tolerant Node 3.5 Literature Comparison3.6 Summary and Final Remarks; References; PART II EVALUATION; 4 Failure Propagation in Load-Sharing Complex Systems; 4.1 Introduction; 4.2 Building Blocks; 4.2.1 Coarse-grained Modeling; 4.2.2 Abstract Mechanisms Impacting the Failure Occurrence; 4.2.3 Parametric Distributions Revisited; 4.2.4 Exponential Distribution; 4.2.5 Weibull Distribution; 4.2.6 Lognormal Distribution; 4.2.7 Other Distributions; 4.3 Sand Box for Distributed Failures; 4.3.1 Failure Modes; 4.3.2 LOS and Stress Rupture; 4.4 Summary; References 5 Approximating Distributions and Transient Probabilities by Matrix Exponential Distributions and Functions5.1 Introduction; 5.2 Phase Type and Matrix Exponential Distributions; 5.3 Bernstein Polynomials and Expolynomials; 5.4 Application of BEs to Distribution Fitting; 5.5 Application of BEs to Transient Probabilities; 5.6 Conclusions; References; 6 Worst-Case Analysis of Tandem Queueing Systems Using Network Calculus; 6.1 Introduction; 6.2 Basic Network Calculus Modeling: Per-fl ow Scheduling; 6.2.1 Service Curve; 6.2.2 Arrival Curve; 6.2.3 Delay and Backlog Bounds; 6.2.4 Numerical Examples 6.3 Advanced Network Calculus Modeling: Aggregate Multiplexing6.3.1 Aggregate-multiplexing Schemes; 6.4 Tandem Systems Traversed by Several Flows; 6.4.1 Model; 6.4.2 Loss of the Tightness; 6.4.3 Separated-flow Analysis; 6.5 Mathematical Programming Approach; 6.5.1 Blind Multiplexing; 6.5.2 FIFO Multiplexing; 6.6 Related Work; 6.7 Numerical Results; 6.8 Conclusions; References; 7 Cloud Evaluation: Benchmarking and Monitoring; 7.1 Introduction; 7.2 Benchmarking; 7.2.1 Benchamrking State of Art; 7.2.2 Benchmarking Big Data Services; 7.3 Benchmarking with mOSAIC; 7.4 Monitoring 7.4.1 Monitoring Problem Scenarios |
| Record Nr. | UNINA-9910131281903321 |
| Salem, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Quantitative assessments of distributed systems : methodologies and techniques / / edited by Dario Bruneo and Salvatore Distefano ; cover design by Russell Richardson
| Quantitative assessments of distributed systems : methodologies and techniques / / edited by Dario Bruneo and Salvatore Distefano ; cover design by Russell Richardson |
| Pubbl/distr/stampa | Salem, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 |
| Descrizione fisica | 1 online resource (398 p.) |
| Disciplina | 004.029 |
| Collana | Performability Engineering Series |
| Soggetto topico |
Computer systems - Evaluation - Mathematics
System analysis - Mathematics Electronic data processing - Distributed processing - Mathematical models Quantitative research |
| ISBN |
1-119-13113-8
1-119-13115-4 1-119-13114-6 |
| Classificazione | TEC008000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Title Page; Copyright Page; Contents; Preface; PART I VERIFICATION; 1 Modeling and Verification of Distributed Systems Using Markov Decision Processes; 1.1 Introduction; 1.2 Markov Decision Processes; 1.3 Markov Decision Well-Formed Net formalism; 1.4 Case study: Peer-to-Peer Botnets; 1.5 Conclusion; Appendices: Well-formed Net Formalism; A.0.1 Syntax of Basic Predicates; A.0.2 Markings and Enabling; References; 2 Quantitative Analysis of Distributed Systems in Stoklaim: A Tutorial; 2.1 Introduction; 2.2 StoKlaim: Stochastic Klaim; 2.2.1 Klaim in a Nutshell; 2.2.2 Syntactic Categories
2.2.3 StoKlaim Syntax2.2.4 StoKlaim at Work; 2.3 StoKlaim Operational Semantics; 2.3.1 Rate Transition Systems; 2.3.2 StoKlaim: RTS-based Semantics; 2.4 MoSL: Mobile Stochastic Logic; 2.5 jSAM: Java Stochastic Model-Checker; 2.6 Leader Election in StoKlaim; 2.6.1 As far as it can; 2.6.2 Asynchronous Leader Election; 2.7 Concluding Remarks; References; 3 Stochastic Path Properties of Distributed Systems: the CSLTA Approach; 3.1 Introduction; 3.2 The Reference Formalisms for System Definition; 3.3 The Formalism for Path Property Definition: CSLTA; 3.4 CSLTA at work: a Fault-Tolerant Node 3.5 Literature Comparison3.6 Summary and Final Remarks; References; PART II EVALUATION; 4 Failure Propagation in Load-Sharing Complex Systems; 4.1 Introduction; 4.2 Building Blocks; 4.2.1 Coarse-grained Modeling; 4.2.2 Abstract Mechanisms Impacting the Failure Occurrence; 4.2.3 Parametric Distributions Revisited; 4.2.4 Exponential Distribution; 4.2.5 Weibull Distribution; 4.2.6 Lognormal Distribution; 4.2.7 Other Distributions; 4.3 Sand Box for Distributed Failures; 4.3.1 Failure Modes; 4.3.2 LOS and Stress Rupture; 4.4 Summary; References 5 Approximating Distributions and Transient Probabilities by Matrix Exponential Distributions and Functions5.1 Introduction; 5.2 Phase Type and Matrix Exponential Distributions; 5.3 Bernstein Polynomials and Expolynomials; 5.4 Application of BEs to Distribution Fitting; 5.5 Application of BEs to Transient Probabilities; 5.6 Conclusions; References; 6 Worst-Case Analysis of Tandem Queueing Systems Using Network Calculus; 6.1 Introduction; 6.2 Basic Network Calculus Modeling: Per-fl ow Scheduling; 6.2.1 Service Curve; 6.2.2 Arrival Curve; 6.2.3 Delay and Backlog Bounds; 6.2.4 Numerical Examples 6.3 Advanced Network Calculus Modeling: Aggregate Multiplexing6.3.1 Aggregate-multiplexing Schemes; 6.4 Tandem Systems Traversed by Several Flows; 6.4.1 Model; 6.4.2 Loss of the Tightness; 6.4.3 Separated-flow Analysis; 6.5 Mathematical Programming Approach; 6.5.1 Blind Multiplexing; 6.5.2 FIFO Multiplexing; 6.6 Related Work; 6.7 Numerical Results; 6.8 Conclusions; References; 7 Cloud Evaluation: Benchmarking and Monitoring; 7.1 Introduction; 7.2 Benchmarking; 7.2.1 Benchamrking State of Art; 7.2.2 Benchmarking Big Data Services; 7.3 Benchmarking with mOSAIC; 7.4 Monitoring 7.4.1 Monitoring Problem Scenarios |
| Record Nr. | UNINA-9910809546503321 |
| Salem, Massachusetts ; ; Hoboken, New Jersey : , : Scrivener Publishing : , : Wiley, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
