{"product_id":"scalable-computing-and-communications-isbn-9781118162651","title":"Scalable Computing and Communications","description":"\u003cp\u003e\u003cb\u003eReviews the latest advances in the all-important field of scalable computing\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn telecommunications and software engineering, scalability is the ability of a system, network, or process to either handle growing amounts of work in a graceful manner or be enlarged to accommodate that growth. It is a desirable property for many scientific, industrial, and business applications and an important feature for hardware.\u003c\/p\u003e \u003cp\u003eThis immersive book summarizes the latest research achievements in the field of scalable computing and covers new topics that have emerged recently on computing and communications, such as unconventional computing, green and sustainable computing, cloud and volunteer computing, and more. Filled with contributions from world-renowned engineers, researchers, and IT professionals in diverse areas, \u003ci\u003eScalable Computing and Communications\u003c\/i\u003e covers:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCircuit and component design\u003c\/li\u003e \u003cli\u003eOperating systems\u003c\/li\u003e \u003cli\u003eGreen computing\u003c\/li\u003e \u003cli\u003eNetwork-on-chip paradigms\u003c\/li\u003e \u003cli\u003eComputational grids\u003c\/li\u003e \u003cli\u003eHigh-performance computing\u003c\/li\u003e \u003cli\u003eSoftware\u003c\/li\u003e \u003cli\u003eNetworking in scalable computing and mobile computing\u003c\/li\u003e \u003cli\u003eNext-generation networking\u003c\/li\u003e \u003cli\u003eCloud computing\u003c\/li\u003e \u003cli\u003ePeer-to-peer systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eScalable Computing and Communications\u003c\/i\u003e is well organized with basic concepts, software infrastructure and middleware, and applications and systems. Filled with numerous case studies, figures, and tables, it is a valuable book that offers great insight into future trends and emerging topics for professionals and students in the field.\u003c\/p\u003e Preface xix \u003cp\u003eContributors xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Scalable Computing and Communications: Past, Present, and Future 1\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYanhui Wu, Kashif Bilal, Samee U. Khan, Lizhe Wang, and Albert Y. Zomaya\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Scalable Computing and Communications 1\u003c\/p\u003e \u003cp\u003eReferences 4\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Reliable Minimum Connected Dominating Sets for Topology Control in Probabilistic Wireless Sensor Networks 7\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJing (Selena) He, Shouling Ji, Yi Pan, and Yingshu Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Topology Control in Wireless Sensor Networks (WSNs) 7\u003c\/p\u003e \u003cp\u003e2.2 DS-Based Topology Control 10\u003c\/p\u003e \u003cp\u003e2.3 Deterministic WSNs and Probabilistic WSNs 12\u003c\/p\u003e \u003cp\u003e2.4 Reliable MCDS Problem 13\u003c\/p\u003e \u003cp\u003e2.5 A GA to Construct RMCDS-GA 17\u003c\/p\u003e \u003cp\u003e2.6 Performance Evaluation 26\u003c\/p\u003e \u003cp\u003e2.7 Conclusions 27\u003c\/p\u003e \u003cp\u003eReferences 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Peer Selection Schemes in Scalable P2P Video Streaming Systems 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eXin Jin and Yu-Kwong Kwok\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 31\u003c\/p\u003e \u003cp\u003e3.2 Overlay Structures 32\u003c\/p\u003e \u003cp\u003e3.3 Peer Selection for Overlay Construction 34\u003c\/p\u003e \u003cp\u003e3.4 A Game Theoretic Perspective on Peer Selection 45\u003c\/p\u003e \u003cp\u003e3.5 Discussion and Future Work 47\u003c\/p\u003e \u003cp\u003e3.6 Summary 48\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Multicore and Many-Core Computing 55\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIoannis E. Venetis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 55\u003c\/p\u003e \u003cp\u003e4.2 Architectural Options for Multicore Systems 60\u003c\/p\u003e \u003cp\u003e4.3 Multicore Architecture Examples 64\u003c\/p\u003e \u003cp\u003e4.4 Programming Multicore Architectures 67\u003c\/p\u003e \u003cp\u003e4.5 Many-Core Architectures 74\u003c\/p\u003e \u003cp\u003e4.6 Many-Core Architecture Examples 75\u003c\/p\u003e \u003cp\u003e4.7 Summary 77\u003c\/p\u003e \u003cp\u003eReferences 77\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Scalable Computing on Large Heterogeneous CPU\/GPU Supercomputers 81\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFengshun Lu, Kaijun Ren, Junqiang Song, and Jinjun Chen\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 81\u003c\/p\u003e \u003cp\u003e5.2 Heterogeneous Computing Environments 82\u003c\/p\u003e \u003cp\u003e5.3 Scalable Programming Patterns for Large GPU Clusters 84\u003c\/p\u003e \u003cp\u003e5.4 Hybrid Implementations 87\u003c\/p\u003e \u003cp\u003e5.5 Experimental Results 89\u003c\/p\u003e \u003cp\u003e5.6 Conclusions 94\u003c\/p\u003e \u003cp\u003eAcknowledgments 94\u003c\/p\u003e \u003cp\u003eReferences 94\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Diagnosability of Multiprocessor Systems 97\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eChia-Wei Lee and Sun-Yuan Hsieh\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 97\u003c\/p\u003e \u003cp\u003e6.2 Fundamental Concepts 98\u003c\/p\u003e \u003cp\u003e6.3 Diagnosability of (1,2)-MCNS under PMC Model 103\u003c\/p\u003e \u003cp\u003e6.4 Diagnosability of 2-MCNS under MM* Model 105\u003c\/p\u003e \u003cp\u003e6.5 Application to Multiprocessor Systems 110\u003c\/p\u003e \u003cp\u003e6.6 Concluding Remarks 122\u003c\/p\u003e \u003cp\u003eReferences 122\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. A Performance Analysis Methodology for MultiCore, Multithreaded Processors 125\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMiao Ju, Hun Jung, and Hao Che\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 125\u003c\/p\u003e \u003cp\u003e7.2 Methodology 126\u003c\/p\u003e \u003cp\u003e7.3 Simulation Tool (ST) 130\u003c\/p\u003e \u003cp\u003e7.4 Analytic Modeling Technique 132\u003c\/p\u003e \u003cp\u003e7.5 Testing 136\u003c\/p\u003e \u003cp\u003e7.6 Related Work 139\u003c\/p\u003e \u003cp\u003e7.7 Conclusions and Future Work 141\u003c\/p\u003e \u003cp\u003eReferences 141\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. The Future in Mobile Multicore Computing 145\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eBlake Hurd, Chiu C. Tan, and Jie Wu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 145\u003c\/p\u003e \u003cp\u003e8.2 Background 146\u003c\/p\u003e \u003cp\u003e8.3 Hardware Initiatives 148\u003c\/p\u003e \u003cp\u003e8.4 Software Initiatives 151\u003c\/p\u003e \u003cp\u003e8.5 Additional Discussion 152\u003c\/p\u003e \u003cp\u003e8.6 Future Trends 153\u003c\/p\u003e \u003cp\u003e8.7 Conclusion 154\u003c\/p\u003e \u003cp\u003eReferences 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Modeling and Algorithms for Scalable and Energy-Efficient Execution on Multicore Systems 157\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eDong Li, Dimitrios S. Nikolopoulos, and Kirk W. Cameron\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 157\u003c\/p\u003e \u003cp\u003e9.2 Model-Based Hybrid Message-Passing Interface (MPI)\/OpenMP Power-Aware Computing 158\u003c\/p\u003e \u003cp\u003e9.3 Power-Aware MPI Task Aggregation Prediction 170\u003c\/p\u003e \u003cp\u003e9.4 Conclusions 181\u003c\/p\u003e \u003cp\u003eReferences 182\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Cost Optimization for Scalable Communication in Wireless Networks with Movement-Based Location Management 185\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKeqin Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 185\u003c\/p\u003e \u003cp\u003e10.2 Background Information 187\u003c\/p\u003e \u003cp\u003e10.3 Cost Measure and Optimization for a Single User 190\u003c\/p\u003e \u003cp\u003e10.4 Cost Optimization with Location Update Constraint 192\u003c\/p\u003e \u003cp\u003e10.5 Cost Optimization with Terminal Paging Constraint 196\u003c\/p\u003e \u003cp\u003e10.6 Numerical Data 201\u003c\/p\u003e \u003cp\u003e10.7 Concluding Remarks 206\u003c\/p\u003e \u003cp\u003eReferences \/ 206\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. A Framework for Semiautomatic Explicit Parallelization 209\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eRitu Arora, Purushotham Bangalore, and Marjan Mernik\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 209\u003c\/p\u003e \u003cp\u003e11.2 Explicit Parallelization Using MPI 210\u003c\/p\u003e \u003cp\u003e11.3 Building Blocks of FraSPA 211\u003c\/p\u003e \u003cp\u003e11.4 Evaluation of FraSPA through Case Studies 215\u003c\/p\u003e \u003cp\u003e11.5 Lessons Learned 221\u003c\/p\u003e \u003cp\u003e11.6 Related Work 222\u003c\/p\u003e \u003cp\u003e11.7 Summary 224\u003c\/p\u003e \u003cp\u003eReferences 224\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Fault Tolerance and Transmission Reliability in Wireless Networks 227\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eWolfgang W. Bein and Doina Bein\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction: Reliability Issues in Wireless and Sensor Networks 227\u003c\/p\u003e \u003cp\u003e12.2 Reliability and Fault Tolerance of Coverage Models for Sensor Networks 230\u003c\/p\u003e \u003cp\u003e12.3 Fault-Tolerant k-Fold Pivot Routing in Wireless Sensor Networks 238\u003c\/p\u003e \u003cp\u003e12.4 Impact of Variable Transmission Range in All-Wireless Networks 244\u003c\/p\u003e \u003cp\u003e12.5 Conclusions and Open Problems 250\u003c\/p\u003e \u003cp\u003eReferences \/ 251\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Optimizing and Tuning Scientifi c Codes 255\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eQing Yi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 255\u003c\/p\u003e \u003cp\u003e13.2 An Abstract View of the Machine Architecture 256\u003c\/p\u003e \u003cp\u003e13.3 Optimizing Scientifi c Codes 256\u003c\/p\u003e \u003cp\u003e13.4 Empirical Tuning of Optimizations 262\u003c\/p\u003e \u003cp\u003e13.5 Related Work 272\u003c\/p\u003e \u003cp\u003e13.6 Summary and Future Work 273\u003c\/p\u003e \u003cp\u003eAcknowledgments 273\u003c\/p\u003e \u003cp\u003eReferences 273\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Privacy and Confi dentiality in Cloud Computing 277\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eKhaled M. Khan and Qutaibah Malluhi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 277\u003c\/p\u003e \u003cp\u003e14.2 Cloud Stakeholders and Computational Assets 278\u003c\/p\u003e \u003cp\u003e14.3 Data Privacy and Trust 280\u003c\/p\u003e \u003cp\u003e14.4 A Cloud Computing Example 281\u003c\/p\u003e \u003cp\u003e14.5 Conclusion 288\u003c\/p\u003e \u003cp\u003eAcknowledgments 288\u003c\/p\u003e \u003cp\u003eReferences 288\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Reputation Management Systems for Peer-to-Peer Networks 291\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eFang Qi, Haiying Shen, Harrison Chandler, Guoxin Liu, and Ze Li\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 291\u003c\/p\u003e \u003cp\u003e15.2 Reputation Management Systems 292\u003c\/p\u003e \u003cp\u003e15.3 Case Study of Reputation Systems 307\u003c\/p\u003e \u003cp\u003e15.4 Open Problems 316\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 316\u003c\/p\u003e \u003cp\u003eAcknowledgments 317\u003c\/p\u003e \u003cp\u003eReferences 317\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16. Toward a Secure Fragment Allocation of Files in Heterogeneous Distributed Systems 321\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYun Tian, Mohammed I. Alghamdi, Xiaojun Ruan, Jiong Xie, and Xiao Qin\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 321\u003c\/p\u003e \u003cp\u003e16.2 Related Work 323\u003c\/p\u003e \u003cp\u003e16.3 System and Threat Models 325\u003c\/p\u003e \u003cp\u003e16.4 S-FAS: A Secure Fragment Allocation Scheme 327\u003c\/p\u003e \u003cp\u003e16.5 Assurance Models 329\u003c\/p\u003e \u003cp\u003e16.6 Sap Allocation Principles and Prototype 332\u003c\/p\u003e \u003cp\u003e16.7 Evaluation of System Assurance and Performance 333\u003c\/p\u003e \u003cp\u003e16.8 Conclusion 339\u003c\/p\u003e \u003cp\u003eAcknowledgments 341\u003c\/p\u003e \u003cp\u003eReferences 341\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17. Adopting Compression in Wireless Sensor Networks 343\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eXi Deng and Yuanyuan Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 343\u003c\/p\u003e \u003cp\u003e17.2 Compression in Sensor Nodes 345\u003c\/p\u003e \u003cp\u003e17.3 Compression Effect on Packet Delay 348\u003c\/p\u003e \u003cp\u003e17.4 Online Adaptive Compression Algorithm 350\u003c\/p\u003e \u003cp\u003e17.5 Performance Evaluations 360\u003c\/p\u003e \u003cp\u003e17.6 Summary 362\u003c\/p\u003e \u003cp\u003eReferences 363\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18. GFOG: Green and Flexible Opportunistic Grids 365\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eHarold Castro, Mario Villamizar, German Sotelo, Cesar O. Diaz, Johnatan Pecero, Pascal Bouvry, and Samee U. Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 365\u003c\/p\u003e \u003cp\u003e18.2 Related Work 366\u003c\/p\u003e \u003cp\u003e18.3 UnaGrid Infrastructure 369\u003c\/p\u003e \u003cp\u003e18.4 Energy Consumption Model 372\u003c\/p\u003e \u003cp\u003e18.5 Experimental Results 374\u003c\/p\u003e \u003cp\u003e18.6 Conclusions and Future Work 382\u003c\/p\u003e \u003cp\u003eReferences 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19. Maximizing Real-Time System Utilization by Adjusting Task Computation Times 387\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eNasro Min-Allah, Samee Ullah Khan, Yongji Wang, Joanna Kolodziej, and Nasir Ghani\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 387\u003c\/p\u003e \u003cp\u003e19.2 Expressing Task Schedulability in Polylinear Surfaces 389\u003c\/p\u003e \u003cp\u003e19.3 Task Execution Time Adjustment Based on the P-Bound 391\u003c\/p\u003e \u003cp\u003e19.4 Conclusions 393\u003c\/p\u003e \u003cp\u003eAcknowledgments 393\u003c\/p\u003e \u003cp\u003eReferences 393\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20. Multilevel Exploration of the Optimization Landscape through Dynamical Fitness for Grid Scheduling 395\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJoanna Kolodziej\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction 395\u003c\/p\u003e \u003cp\u003e20.2 Statement of the Problem 397\u003c\/p\u003e \u003cp\u003e20.3 General Characteristics of the Optimization Landscape 399\u003c\/p\u003e \u003cp\u003e20.4 Multilevel Metaheuristic Schedulers 402\u003c\/p\u003e \u003cp\u003e20.5 Empirical Analysis 408\u003c\/p\u003e \u003cp\u003e20.6 Conclusions 417\u003c\/p\u003e \u003cp\u003eReferences 417\u003c\/p\u003e \u003cp\u003e\u003cb\u003e21. Implementing Pointer Jumping for Exact Inference on Many-Core Systems 419\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eYinglong Xia, Nam Ma, and Viktor K. Prasanna\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e21.1 Introduction 419\u003c\/p\u003e \u003cp\u003e21.2 Background 420\u003c\/p\u003e \u003cp\u003e21.3 Related Work 422\u003c\/p\u003e \u003cp\u003e21.4 Pointer Jumping-Based Algorithms for Scheduling Exact Inference 423\u003c\/p\u003e \u003cp\u003e21.5 Analysis with Respect to Many-Core Processors 424\u003c\/p\u003e \u003cp\u003e21.6 From Exact Inference to Generic Directed Acyclic Graph (DAG)-Structured Computations 427\u003c\/p\u003e \u003cp\u003e21.7 Experiments 428\u003c\/p\u003e \u003cp\u003e21.8 Conclusions 434\u003c\/p\u003e \u003cp\u003eReferences 435\u003c\/p\u003e \u003cp\u003e\u003cb\u003e22. Performance Optimization of Scientifi c Applications Using an Autonomic Computing Approach 437\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eIoana Banicescu, Florina M. Ciorba, and Srishti Srivastava\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e22.1 Introduction 437\u003c\/p\u003e \u003cp\u003e22.2 Scientifi c Applications and Their Performance 439\u003c\/p\u003e \u003cp\u003e22.3 Load Balancing via DLS 441\u003c\/p\u003e \u003cp\u003e22.4 The Use of Machine Learning in Improving the Performance of Scientifi c Applications 441\u003c\/p\u003e \u003cp\u003e22.5 Design Strategies and an Integrated Framework 445\u003c\/p\u003e \u003cp\u003e22.6 Experimental Results, Analysis, and Evaluation 455\u003c\/p\u003e \u003cp\u003e22.7 Conclusions, Future Work, and Open Problems 462\u003c\/p\u003e \u003cp\u003eAcknowledgments 463\u003c\/p\u003e \u003cp\u003eReferences 463\u003c\/p\u003e \u003cp\u003e\u003cb\u003e23. A Survey of Techniques for Improving Search Engine Scalability through Profi ling, Prediction, and Prefetching of Query Results 467\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eC. Shaun Wagner, Sahra Sedigh, Ali R. Hurson, and Behrooz Shirazi\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e23.1 Introduction 467\u003c\/p\u003e \u003cp\u003e23.2 Modeling User Behavior 472\u003c\/p\u003e \u003cp\u003e23.3 Grouping Users into Neighborhoods of Similarity 474\u003c\/p\u003e \u003cp\u003e23.4 Similarity Metrics 481\u003c\/p\u003e \u003cp\u003e23.5 Conclusion and Future Work 497\u003c\/p\u003e \u003cp\u003eAppendix A Comparative Analysis of Comparison Algorithms 498\u003c\/p\u003e \u003cp\u003eAppendix B Most Popular Searches 501\u003c\/p\u003e \u003cp\u003eReferences 502\u003c\/p\u003e \u003cp\u003e\u003cb\u003e24. KNN Queries in Mobile Sensor Networks 507\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eWei-Guang Teng and Kun-Ta Chuang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e24.1 Introduction 507\u003c\/p\u003e \u003cp\u003e24.2 Preliminaries and Infrastructure-Based KNN Queries 509\u003c\/p\u003e \u003cp\u003e24.3 Infrastructure-Free KNN Queries 511\u003c\/p\u003e \u003cp\u003e24.4 Future Research Directions 519\u003c\/p\u003e \u003cp\u003e24.5 Conclusions 519\u003c\/p\u003e \u003cp\u003eReferences 520\u003c\/p\u003e \u003cp\u003e\u003cb\u003e25. Data Partitioning for Designing and Simulating Efficient Huge Databases 523\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eLadjel Bellatreche, Kamel Boukhalfa, Pascal Richard, and Soumia Benkrid\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e25.1 Introduction 523\u003c\/p\u003e \u003cp\u003e25.2 Background and Related Work 527\u003c\/p\u003e \u003cp\u003e25.3 Fragmentation Methodology 532\u003c\/p\u003e \u003cp\u003e25.4 Hardness Study 535\u003c\/p\u003e \u003cp\u003e25.5 Proposed Selection Algorithms 538\u003c\/p\u003e \u003cp\u003e25.6 Impact of HP on Data Warehouse Physical Design 544\u003c\/p\u003e \u003cp\u003e25.7 Experimental Studies 549\u003c\/p\u003e \u003cp\u003e25.8 Physical Design Simulator Tool 553\u003c\/p\u003e \u003cp\u003e25.9 Conclusion and Perspectives 559\u003c\/p\u003e \u003cp\u003eReferences 560\u003c\/p\u003e \u003cp\u003e\u003cb\u003e26. Scalable Runtime Environments for Large-Scale Parallel Applications 563\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eCamille Coti and Franck Cappello\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e26.1 Introduction 563\u003c\/p\u003e \u003cp\u003e26.2 Goals of a Runtime Environment 565\u003c\/p\u003e \u003cp\u003e26.3 Communication Infrastructure 567\u003c\/p\u003e \u003cp\u003e26.4 Application Deployment 571\u003c\/p\u003e \u003cp\u003e26.5 Fault Tolerance and Robustness 577\u003c\/p\u003e \u003cp\u003e26.6 Case Studies 582\u003c\/p\u003e \u003cp\u003e26.7 Conclusion 586\u003c\/p\u003e \u003cp\u003eReferences 587\u003c\/p\u003e \u003cp\u003e\u003cb\u003e27. Increasing Performance through Optimization on APU 591\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMatthew Doerksen, Parimala Thulasiraman, and Ruppa Thulasiram\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e27.1 Introduction 591\u003c\/p\u003e \u003cp\u003e27.2 Heterogeneous Architectures 591\u003c\/p\u003e \u003cp\u003e27.3 Related Work 597\u003c\/p\u003e \u003cp\u003e27.4 OpenCL, CUDA of the Future 600\u003c\/p\u003e \u003cp\u003e27.5 Simple Introduction to OpenCL Programming 604\u003c\/p\u003e \u003cp\u003e27.6 Performance and Optimization Summary 607\u003c\/p\u003e \u003cp\u003e27.7 Application 607\u003c\/p\u003e \u003cp\u003e27.8 Summary 609\u003c\/p\u003e \u003cp\u003eAppendix 609\u003c\/p\u003e \u003cp\u003eReferences 612\u003c\/p\u003e \u003cp\u003e\u003cb\u003e28. Toward Optimizing Cloud Computing: An Example of Optimization under Uncertainty 613\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eVladik Kreinovich\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e28.1 Cloud Computing: Why We Need It and How We Can Make It Most Efficient 613\u003c\/p\u003e \u003cp\u003e28.2 Optimal Server Placement Problem: First Approximation 614\u003c\/p\u003e \u003cp\u003e28.3 Server Placement in Cloud Computing: Toward a More Realistic Model 618\u003c\/p\u003e \u003cp\u003e28.4 Predicting Cloud Growth: Formulation of the Problem and Our Approach to Solving This Problem 620\u003c\/p\u003e \u003cp\u003e28.5 Predicting Cloud Growth: First Approximation 621\u003c\/p\u003e \u003cp\u003e28.6 Predicting Cloud Growth: Second Approximation 622\u003c\/p\u003e \u003cp\u003e28.7 Predicting Cloud Growth: Third Approximation 623\u003c\/p\u003e \u003cp\u003e28.8 Conclusions and Future Work 625\u003c\/p\u003e \u003cp\u003eAcknowledgments 625\u003c\/p\u003e \u003cp\u003eAppendix: Description of Expenses Related to Cloud Computing 626\u003c\/p\u003e \u003cp\u003eReferences 626\u003c\/p\u003e \u003cp\u003e\u003cb\u003e29. Modeling of Scalable Embedded Systems 629\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eArslan Munir, Sanjay Ranka, and Ann Gordon-Ross\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e29.1 Introduction 629\u003c\/p\u003e \u003cp\u003e29.2 Embedded System Applications 631\u003c\/p\u003e \u003cp\u003e29.3 Embedded Systems: Hardware and Software 634\u003c\/p\u003e \u003cp\u003e29.4 Modeling: An Integral Part of the Embedded System Design Flow 638\u003c\/p\u003e \u003cp\u003e29.5 Single- and Multiunit Embedded System Modeling 644\u003c\/p\u003e \u003cp\u003e29.6 Conclusions 654\u003c\/p\u003e \u003cp\u003eAcknowledgments 655\u003c\/p\u003e \u003cp\u003eReferences 655\u003c\/p\u003e \u003cp\u003e\u003cb\u003e30. Scalable Service Composition in Pervasive Computing 659\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJoanna Siebert and Jiannong Cao\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e30.1 Introduction 659\u003c\/p\u003e \u003cp\u003e30.2 Service Composition Framework 660\u003c\/p\u003e \u003cp\u003e30.3 Approaches and Techniques for Scalable Service Composition in PvCE 664\u003c\/p\u003e \u003cp\u003e30.4 Conclusions 671\u003c\/p\u003e \u003cp\u003eReferences 671\u003c\/p\u003e \u003cp\u003e\u003cb\u003e31. Virtualization Techniques for Graphics Processing Units 675\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePavan Balaji, Qian Zhu, and Wu-Chun Feng\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e31.1 Introduction 675\u003c\/p\u003e \u003cp\u003e31.2 Background 677\u003c\/p\u003e \u003cp\u003e31.3 VOCL Framework 677\u003c\/p\u003e \u003cp\u003e31.4 VOCL Optimizations 682\u003c\/p\u003e \u003cp\u003e31.5 Experimental Evaluation 687\u003c\/p\u003e \u003cp\u003e31.6 Related Work 696\u003c\/p\u003e \u003cp\u003e31.7 Concluding Remarks 696\u003c\/p\u003e \u003cp\u003eReferences 697\u003c\/p\u003e \u003cp\u003e\u003cb\u003e32. Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach 699\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eGeorge Bosilca, Aurelien Bouteiller, Anthony Danalis, Thomas Herault, Piotr Luszczek, and Jack J. Dongara\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e32.1 Introduction and Motivation 699\u003c\/p\u003e \u003cp\u003e32.2 Distributed Datafl ow by Symbolic Evaluation 701\u003c\/p\u003e \u003cp\u003e32.3 The DAGuE Datafl ow Runtime 705\u003c\/p\u003e \u003cp\u003e32.4 Datafl ow Representation 709\u003c\/p\u003e \u003cp\u003e32.5 Programming Linear Algebra with DAGuE 716\u003c\/p\u003e \u003cp\u003e32.6 Performance Evaluation 728\u003c\/p\u003e \u003cp\u003e32.7 Conclusion 731\u003c\/p\u003e \u003cp\u003e32.8 Summary 732\u003c\/p\u003e \u003cp\u003eReferences 733\u003c\/p\u003e \u003cp\u003e\u003cb\u003e33. Fault-Tolerance Techniques for Scalable Computing 737\u003c\/b\u003e\u003cbr\u003e\u003ci\u003ePavan Balaji, Darius Buntinas, and Dries Kimpe\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e33.1 Introduction and Trends in Large-Scale Computing Systems 737\u003c\/p\u003e \u003cp\u003e33.2 Hardware Features for Resilience 738\u003c\/p\u003e \u003cp\u003e33.3 Systems Software Features for Resilience 743\u003c\/p\u003e \u003cp\u003e33.4 Application or Domain-Specifi c Fault-Tolerance Techniques 748\u003c\/p\u003e \u003cp\u003e33.5 Summary 753\u003c\/p\u003e \u003cp\u003eReferences 753\u003c\/p\u003e \u003cp\u003e\u003cb\u003e34. Parallel Programming Models for Scalable Computing 759\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJames Dinan and Pavan Balaji\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e34.1 Introduction to Parallel Programming Models 759\u003c\/p\u003e \u003cp\u003e34.2 The Message-Passing Interface (MPI) 761\u003c\/p\u003e \u003cp\u003e34.3 Partitioned Global Address Space (PGAS) Models 765\u003c\/p\u003e \u003cp\u003e34.4 Task-Parallel Programming Models 769\u003c\/p\u003e \u003cp\u003e34.5 High-Productivity Parallel Programming Models 772\u003c\/p\u003e \u003cp\u003e34.6 Summary and Concluding Remarks 775\u003c\/p\u003e \u003cp\u003eAcknowledgment 775\u003c\/p\u003e \u003cp\u003eReferences 775\u003c\/p\u003e \u003cp\u003e\u003cb\u003e35. Grid Simulation Tools for Job Scheduling and Data File Replication 777\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eJavid Taheri, Albert Y. Zomaya, and Samee U. Khan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e35.1 Introduction 777\u003c\/p\u003e \u003cp\u003e35.2 Simulation Platforms 779\u003c\/p\u003e \u003cp\u003e35.3 Problem Statement: Data-Aware Job Scheduling (DAJS) 792\u003c\/p\u003e \u003cp\u003eReferences 795\u003c\/p\u003e \u003cp\u003eIndex 799\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eSAMEE U. KHAN, PhD,\u003c\/b\u003e is Assistant Professor of Electrical and Computer Engineering at North Dakota State University. He is the founding director of the bi-institutional and multi-departmental NDSU-CIIT Green Computing and Communications Laboratory (GCC Lab) and an Adjunct Professor of Computer Science, COMSATS Institute of Information Technology, Pakistan.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eALBERT Y. ZOMAYA, PhD,\u003c\/b\u003e is the Chair Professor of High Performance Computing and Networking, and Australian Research Council Professorial Fellow in the School of Information Technologies, The University of Sydney. He is also the Director of the Centre for Distributed and High Performance Computing as well as the Series Editor for the Wiley Series on Parallel and Distributed Computing.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eLIZHE WANG, PhD,\u003c\/b\u003e is a Professor at the Center for Earth Observation and Digital Earth, Chinese Academy of Sciences. He is the ChuTian Scholar Chair Professor in the School of Computer, China University of Geosciences. A senior member of the IEEE, professional member of ACM, and member of the IEEE Computer Society, Dr. Wang has published six books and more than fifty technical papers.\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eReviews the latest advances in the all-important field of scalable computing\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn telecommunications and software engineering, scalability is the ability of a system, network, or process to either handle growing amounts of work in a graceful manner or be enlarged to accommodate that growth. It is a desirable property for many scientific, industrial, and business applications and an important feature for hardware.\u003c\/p\u003e \u003cp\u003eThis immersive book summarizes the latest research achievements in the field of scalable computing and covers new topics that have emerged recently on computing and communications, such as unconventional computing, green and sustainable computing, cloud and volunteer computing, and more. Filled with contributions from world-renowned engineers, researchers, and IT professionals in diverse areas, \u003ci\u003eScalable Computing and Communications\u003c\/i\u003e covers:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eCircuit and component design\u003c\/li\u003e \u003cli\u003eOperating systems\u003c\/li\u003e \u003cli\u003eGreen computing\u003c\/li\u003e \u003cli\u003eNetwork-on-chip paradigms\u003c\/li\u003e \u003cli\u003eComputational grids\u003c\/li\u003e \u003cli\u003eHigh-performance computing\u003c\/li\u003e \u003cli\u003eSoftware\u003c\/li\u003e \u003cli\u003eNetworking in scalable computing and mobile computing\u003c\/li\u003e \u003cli\u003eNext-generation networking\u003c\/li\u003e \u003cli\u003eCloud computing\u003c\/li\u003e \u003cli\u003ePeer-to-peer systems\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eScalable Computing and Communications\u003c\/i\u003e is well organized with basic concepts, software infrastructure and middleware, and applications and systems. Filled with numerous case studies, figures, and tables, it is a valuable book that offers great insight into future trends and emerging topics for professionals and students in the field.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989989310693,"sku":"NP9781118162651","price":179.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118162651.jpg?v=1761786125","url":"https:\/\/k12savings.com\/es\/products\/scalable-computing-and-communications-isbn-9781118162651","provider":"K12savings","version":"1.0","type":"link"}