{"product_id":"quantitative-assessments-of-distributed-systems-9781118595213","title":"Quantitative Assessments of Distributed Systems","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eDistributed systems employed in critical infrastructures must fulfill dependability, timeliness, and performance specifications. Since these systems most often operate in an unpredictable environment, their design and maintenance require quantitative evaluation of deterministic and probabilistic timed models. This need gave birth to an abundant literature devoted to formal modeling languages combined with analytical and simulative solution techniques\u003c\/p\u003e \u003cp\u003eThe aim of the book is to provide an overview of techniques and methodologies dealing with such specific issues in the context of distributed systems and covering aspects such as performance evaluation, reliability\/availability, energy efficiency, scalability, and sustainability. Specifically, techniques for checking and verifying if and how a distributed system satisfies the requirements, as well as how to properly evaluate non-functional aspects, or how to optimize the overall behavior of the system, are all discussed in the bo\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003ePreface xiii\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003ePART I VERIFICATION\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003e1. Modeling and Verification of Distributed Systems Using Markov Decision Processes 3\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e1.1 Introduction 4\u003cbr\u003e\u003cbr\u003e1.2 Markov Decision Processes 5\u003cbr\u003e\u003cbr\u003e1.3 Markov Decision Well-Formed Net formalism 8\u003cbr\u003e\u003cbr\u003e1.4 Case study: Peer-to-Peer Botnets 10\u003cbr\u003e\u003cbr\u003e1.5 Conclusion 18\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAppendices: Well-formed Net Formalism 21\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003eA.1.1 Syntax of Basic Predicates 22\u003cbr\u003e\u003cbr\u003eA.1.2 Markings and Enabling 23\u003cbr\u003e\u003cbr\u003eReferences 25\u003cbr\u003e\u003cbr\u003e\u003cb\u003e2 Quantitative Analysis of Distributed Systems in Stoklaim: A Tutorial 27\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e2.1 Introduction 28\u003cbr\u003e\u003cbr\u003e2.2 StoKlaim: Stochastic Klaim 29\u003cbr\u003e\u003cbr\u003e2.3 StoKlaim Operational Semantics 34\u003cbr\u003e\u003cbr\u003e2.4 MoSL: Mobile Stochastic Logic 43\u003cbr\u003e\u003cbr\u003e2.5 jSAM: Java Stochastic Model-Checker 47\u003cbr\u003e\u003cbr\u003e2.6 Leader Election in StoKlaim 49\u003cbr\u003e\u003cbr\u003e2.7 Concluding Remarks 52\u003cbr\u003e\u003cbr\u003eReferences 53\u003cbr\u003e\u003cbr\u003e\u003cb\u003e3 Stochastic Path Properties of Distributed Systems: the CSLTA Approach 57\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e3.1 Introduction 58\u003cbr\u003e\u003cbr\u003e3.2 The Reference Formalisms for System Definition 59\u003cbr\u003e\u003cbr\u003e3.3 The Formalism for Path Property Definition: CSLTA 61\u003cbr\u003e\u003cbr\u003e3.4 CSLTA at work: a Fault-Tolerant Node 67\u003cbr\u003e\u003cbr\u003e3.5 Literature Comparison 71\u003cbr\u003e\u003cbr\u003e3.6 Summary and Final Remarks 73\u003cbr\u003e\u003cbr\u003eReferences 75\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePART II  EVALUATION\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003e4 Failure Propagation in Load-Sharing Complex Systems 81\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e4.1 Introduction 82\u003cbr\u003e\u003cbr\u003e4.2 Building Blocks 84\u003cbr\u003e\u003cbr\u003e4.3 Sand Box for Distributed Failures 89\u003cbr\u003e\u003cbr\u003e4.4 Summary 102\u003cbr\u003e\u003cbr\u003eReferences 103\u003cbr\u003e\u003cbr\u003e\u003cb\u003e5 Approximating Distributions and Transient Probabilities by Matrix Exponential Distributions and Functions 107\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e5.1 Introduction 108\u003cbr\u003e\u003cbr\u003e5.2 Phase Type and Matrix Exponential Distributions 109\u003cbr\u003e\u003cbr\u003e5.3 Bernstein Polynomials and Expolynomials 114\u003cbr\u003e\u003cbr\u003e5.4 Application of BEs to Distribution Fitting 116\u003cbr\u003e\u003cbr\u003e5.5 Application of BEs to Transient Probabilities 121\u003cbr\u003e\u003cbr\u003e5.6 Conclusions 124\u003cbr\u003e\u003cbr\u003eReferences 125\u003cbr\u003e\u003cbr\u003e\u003cb\u003e6 Worst-Case Analysis of Tandem Queueing Systems Using Network Calculus 129\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e6.1 Introduction 130\u003cbr\u003e\u003cbr\u003e6.2 Basic Network Calculus Modeling: Per-flow Scheduling 132\u003cbr\u003e\u003cbr\u003e6.3 Advanced Network Calculus Modeling: Aggregate Multiplexing 148\u003cbr\u003e\u003cbr\u003e6.4 Tandem Systems Traversed by Several Flows 152\u003cbr\u003e\u003cbr\u003e6.5 Mathematical Programming Approach 154\u003cbr\u003e\u003cbr\u003e6.6 Related Work 165\u003cbr\u003e\u003cbr\u003e6.7 Numerical Results 166\u003cbr\u003e\u003cbr\u003e6.8 Conclusions 168\u003cbr\u003e\u003cbr\u003eReferences 171\u003cbr\u003e\u003cbr\u003e\u003cb\u003e7 Cloud Evaluation: Benchmarking and Monitoring 175\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e7.1 Introduction 176\u003cbr\u003e\u003cbr\u003e7.2 Benchmarking 176\u003cbr\u003e\u003cbr\u003e7.3 Benchmarking with mOSAIC 184\u003cbr\u003e\u003cbr\u003e7.4 Monitoring 185\u003cbr\u003e\u003cbr\u003e7.5 Cloud Monitoring in mOSAIC?s Cloud Agency 191\u003cbr\u003e\u003cbr\u003e7.6 Conclusions 193\u003cbr\u003e\u003cbr\u003eReferences 195\u003cbr\u003e\u003cbr\u003e\u003cb\u003e8 Multiformalism and Multisolution Strategies for Systems Performance  201\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e8.1 Introduction 202\u003cbr\u003e\u003cbr\u003e8.2 Multiformalism and Multisolution 203\u003cbr\u003e\u003cbr\u003e8.3 Choosing the Right Strategy 205\u003cbr\u003e\u003cbr\u003e8.4 Learning by the Experience 206\u003cbr\u003e\u003cbr\u003e8.5 Conclusions and Perspectives 218\u003cbr\u003e\u003cbr\u003eReferences 219\u003cbr\u003e\u003cbr\u003e\u003cb\u003ePART III OPTIMIZATION AND SUSTAINABILITY\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e\u003cb\u003e9 Quantitative Assessment of Distributed Networks Through Hybrid Stochastic Modeling 225\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e9.1 Introduction 226\u003cbr\u003e\u003cbr\u003e9.2 Modeling of Complex Systems 228\u003cbr\u003e\u003cbr\u003e9.3 Performance Evaluation of KNXnet\/IP Networks Flow Control Mechanism 234\u003cbr\u003e\u003cbr\u003e9.4 LCII: On-line Risk Estimation of A Power-Telco Network 248\u003cbr\u003e\u003cbr\u003e9.5 Conclusion 259\u003cbr\u003e\u003cbr\u003eReferences 261\u003cbr\u003e\u003cbr\u003e\u003cb\u003e10 Design of IT Infrastructures of Data Centers: An Approach Based on Business and Technical Metrics 265\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e10.1 Introduction 266\u003cbr\u003e\u003cbr\u003e10.2 Fundamental Concepts 267\u003cbr\u003e\u003cbr\u003e10.3 Business-Oriented Models 270\u003cbr\u003e\u003cbr\u003e10.4 Data Center Infrastructure Models 274\u003cbr\u003e\u003cbr\u003e10.5 Methodology 277\u003cbr\u003e\u003cbr\u003e10.6 Case Study - Data Center Design 283\u003cbr\u003e\u003cbr\u003e10.7 Conclusion 292\u003cbr\u003e\u003cbr\u003eReferences 297\u003cbr\u003e\u003cbr\u003e\u003cb\u003e11 Software Rejuvenation and its Application in Distributed Systems 301\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e11.1 Introduction 302\u003cbr\u003e\u003cbr\u003e11.2 Software rejuvenation scheduling classification 304\u003cbr\u003e\u003cbr\u003e11.3 Software rejuvenation granularity classification 307\u003cbr\u003e\u003cbr\u003e11.4 Methods, policies and metrics of software rejuvenation 314\u003cbr\u003e\u003cbr\u003e11.5 Software rejuvenation in distributed systems 315\u003cbr\u003e\u003cbr\u003e11.6 Summary 318\u003cbr\u003e\u003cbr\u003eReferences 321\u003cbr\u003e\u003cbr\u003e\u003cb\u003e12 Machine Learning Based Dynamic Reconfiguration of  Distributed Data Management Systems 327\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e12.1 Introduction 328\u003cbr\u003e\u003cbr\u003e12.2 Methodologies 330\u003cbr\u003e\u003cbr\u003e12.3 Brief overview of Neural Networks 334\u003cbr\u003e\u003cbr\u003e12.4 System Architecture and Performance Prediction Scheme 336\u003cbr\u003e\u003cbr\u003e12.5 Experimentation 339\u003cbr\u003e\u003cbr\u003e12.6 Conclusions 346\u003cbr\u003e\u003cbr\u003eReferences 347\u003cbr\u003e\u003cbr\u003e\u003cb\u003e13 Going Green with the Networked Cloud: Methodologies and Assessment 351\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003e13.1 Introduction 352\u003cbr\u003e\u003cbr\u003e13.2 Modeling of Data Centre Power Consumption 353\u003cbr\u003e\u003cbr\u003e13.3 Energy Efficiency in the Cloud 356\u003cbr\u003e\u003cbr\u003e13.4 Performance Analysis Methodologies and Tools 361\u003cbr\u003e\u003cbr\u003e13.5 Case Study: Performance Evaluation of Energy Aware Resource Allocation in the Cloud 366\u003cbr\u003e\u003cbr\u003e13.6 Summary 370\u003cbr\u003e\u003cbr\u003eReferences 371\u003cbr\u003e\u003cbr\u003e\u003cb\u003eIndex 375\u003c\/b\u003e\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default 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