Preface xix Contributors xxi 1. Scalable Computing and Communications: Past, Present, and Future 1Yanhui Wu, Kashif Bilal, Samee U. Khan, Lizhe Wang, and Albert Y. Zomaya 1.1 Scalable Computing and Communications 1 References 4 2. Reliable Minimum Connected Dominating Sets for Topology Control in Probabilistic Wireless Sensor Networks 7Jing (Selena) He, Shouling Ji, Yi Pan, and Yingshu Li 2.1 Topology Control in Wireless Sensor Networks (WSNs) 7 2.2 DS-Based Topology Control 10 2.3 Deterministic WSNs and Probabilistic WSNs 12 2.4 Reliable MCDS Problem 13 2.5 A GA to Construct RMCDS-GA 17 2.6 Performance Evaluation 26 2.7 Conclusions 27 References 28 3. Peer Selection Schemes in Scalable P2P Video Streaming Systems 31Xin Jin and Yu-Kwong Kwok 3.1 Introduction 31 3.2 Overlay Structures 32 3.3 Peer Selection for Overlay Construction 34 3.4 A Game Theoretic Perspective on Peer Selection 45 3.5 Discussion and Future Work 47 3.6 Summary 48 References 49 4. Multicore and Many-Core Computing 55Ioannis E. Venetis 4.1 Introduction 55 4.2 Architectural Options for Multicore Systems 60 4.3 Multicore Architecture Examples 64 4.4 Programming Multicore Architectures 67 4.5 Many-Core Architectures 74 4.6 Many-Core Architecture Examples 75 4.7 Summary 77 References 77 5. Scalable Computing on Large Heterogeneous CPU/GPU Supercomputers 81Fengshun Lu, Kaijun Ren, Junqiang Song, and Jinjun Chen 5.1 Introduction 81 5.2 Heterogeneous Computing Environments 82 5.3 Scalable Programming Patterns for Large GPU Clusters 84 5.4 Hybrid Implementations 87 5.5 Experimental Results 89 5.6 Conclusions 94 Acknowledgments 94 References 94 6. Diagnosability of Multiprocessor Systems 97Chia-Wei Lee and Sun-Yuan Hsieh 6.1 Introduction 97 6.2 Fundamental Concepts 98 6.3 Diagnosability of (1,2)-MCNS under PMC Model 103 6.4 Diagnosability of 2-MCNS under MM* Model 105 6.5 Application to Multiprocessor Systems 110 6.6 Concluding Remarks 122 References 122 7. A Performance Analysis Methodology for MultiCore, Multithreaded Processors 125Miao Ju, Hun Jung, and Hao Che 7.1 Introduction 125 7.2 Methodology 126 7.3 Simulation Tool (ST) 130 7.4 Analytic Modeling Technique 132 7.5 Testing 136 7.6 Related Work 139 7.7 Conclusions and Future Work 141 References 141 8. The Future in Mobile Multicore Computing 145Blake Hurd, Chiu C. Tan, and Jie Wu 8.1 Introduction 145 8.2 Background 146 8.3 Hardware Initiatives 148 8.4 Software Initiatives 151 8.5 Additional Discussion 152 8.6 Future Trends 153 8.7 Conclusion 154 References 155 9. Modeling and Algorithms for Scalable and Energy-Efficient Execution on Multicore Systems 157Dong Li, Dimitrios S. Nikolopoulos, and Kirk W. Cameron 9.1 Introduction 157 9.2 Model-Based Hybrid Message-Passing Interface (MPI)/OpenMP Power-Aware Computing 158 9.3 Power-Aware MPI Task Aggregation Prediction 170 9.4 Conclusions 181 References 182 10. Cost Optimization for Scalable Communication in Wireless Networks with Movement-Based Location Management 185Keqin Li 10.1 Introduction 185 10.2 Background Information 187 10.3 Cost Measure and Optimization for a Single User 190 10.4 Cost Optimization with Location Update Constraint 192 10.5 Cost Optimization with Terminal Paging Constraint 196 10.6 Numerical Data 201 10.7 Concluding Remarks 206 References / 206 11. A Framework for Semiautomatic Explicit Parallelization 209Ritu Arora, Purushotham Bangalore, and Marjan Mernik 11.1 Introduction 209 11.2 Explicit Parallelization Using MPI 210 11.3 Building Blocks of FraSPA 211 11.4 Evaluation of FraSPA through Case Studies 215 11.5 Lessons Learned 221 11.6 Related Work 222 11.7 Summary 224 References 224 12. Fault Tolerance and Transmission Reliability in Wireless Networks 227Wolfgang W. Bein and Doina Bein 12.1 Introduction: Reliability Issues in Wireless and Sensor Networks 227 12.2 Reliability and Fault Tolerance of Coverage Models for Sensor Networks 230 12.3 Fault-Tolerant k-Fold Pivot Routing in Wireless Sensor Networks 238 12.4 Impact of Variable Transmission Range in All-Wireless Networks 244 12.5 Conclusions and Open Problems 250 References / 251 13. Optimizing and Tuning Scientifi c Codes 255Qing Yi 13.1 Introduction 255 13.2 An Abstract View of the Machine Architecture 256 13.3 Optimizing Scientifi c Codes 256 13.4 Empirical Tuning of Optimizations 262 13.5 Related Work 272 13.6 Summary and Future Work 273 Acknowledgments 273 References 273 14. Privacy and Confi dentiality in Cloud Computing 277Khaled M. Khan and Qutaibah Malluhi 14.1 Introduction 277 14.2 Cloud Stakeholders and Computational Assets 278 14.3 Data Privacy and Trust 280 14.4 A Cloud Computing Example 281 14.5 Conclusion 288 Acknowledgments 288 References 288 15. Reputation Management Systems for Peer-to-Peer Networks 291Fang Qi, Haiying Shen, Harrison Chandler, Guoxin Liu, and Ze Li 15.1 Introduction 291 15.2 Reputation Management Systems 292 15.3 Case Study of Reputation Systems 307 15.4 Open Problems 316 15.5 Conclusion 316 Acknowledgments 317 References 317 16. Toward a Secure Fragment Allocation of Files in Heterogeneous Distributed Systems 321Yun Tian, Mohammed I. Alghamdi, Xiaojun Ruan, Jiong Xie, and Xiao Qin 16.1 Introduction 321 16.2 Related Work 323 16.3 System and Threat Models 325 16.4 S-FAS: A Secure Fragment Allocation Scheme 327 16.5 Assurance Models 329 16.6 Sap Allocation Principles and Prototype 332 16.7 Evaluation of System Assurance and Performance 333 16.8 Conclusion 339 Acknowledgments 341 References 341 17. Adopting Compression in Wireless Sensor Networks 343Xi Deng and Yuanyuan Yang 17.1 Introduction 343 17.2 Compression in Sensor Nodes 345 17.3 Compression Effect on Packet Delay 348 17.4 Online Adaptive Compression Algorithm 350 17.5 Performance Evaluations 360 17.6 Summary 362 References 363 18. GFOG: Green and Flexible Opportunistic Grids 365Harold Castro, Mario Villamizar, German Sotelo, Cesar O. Diaz, Johnatan Pecero, Pascal Bouvry, and Samee U. Khan 18.1 Introduction 365 18.2 Related Work 366 18.3 UnaGrid Infrastructure 369 18.4 Energy Consumption Model 372 18.5 Experimental Results 374 18.6 Conclusions and Future Work 382 References 382 19. Maximizing Real-Time System Utilization by Adjusting Task Computation Times 387Nasro Min-Allah, Samee Ullah Khan, Yongji Wang, Joanna Kolodziej, and Nasir Ghani 19.1 Introduction 387 19.2 Expressing Task Schedulability in Polylinear Surfaces 389 19.3 Task Execution Time Adjustment Based on the P-Bound 391 19.4 Conclusions 393 Acknowledgments 393 References 393 20. Multilevel Exploration of the Optimization Landscape through Dynamical Fitness for Grid Scheduling 395Joanna Kolodziej 20.1 Introduction 395 20.2 Statement of the Problem 397 20.3 General Characteristics of the Optimization Landscape 399 20.4 Multilevel Metaheuristic Schedulers 402 20.5 Empirical Analysis 408 20.6 Conclusions 417 References 417 21. Implementing Pointer Jumping for Exact Inference on Many-Core Systems 419Yinglong Xia, Nam Ma, and Viktor K. Prasanna 21.1 Introduction 419 21.2 Background 420 21.3 Related Work 422 21.4 Pointer Jumping-Based Algorithms for Scheduling Exact Inference 423 21.5 Analysis with Respect to Many-Core Processors 424 21.6 From Exact Inference to Generic Directed Acyclic Graph (DAG)-Structured Computations 427 21.7 Experiments 428 21.8 Conclusions 434 References 435 22. Performance Optimization of Scientifi c Applications Using an Autonomic Computing Approach 437Ioana Banicescu, Florina M. Ciorba, and Srishti Srivastava 22.1 Introduction 437 22.2 Scientifi c Applications and Their Performance 439 22.3 Load Balancing via DLS 441 22.4 The Use of Machine Learning in Improving the Performance of Scientifi c Applications 441 22.5 Design Strategies and an Integrated Framework 445 22.6 Experimental Results, Analysis, and Evaluation 455 22.7 Conclusions, Future Work, and Open Problems 462 Acknowledgments 463 References 463 23. A Survey of Techniques for Improving Search Engine Scalability through Profi ling, Prediction, and Prefetching of Query Results 467C. Shaun Wagner, Sahra Sedigh, Ali R. Hurson, and Behrooz Shirazi 23.1 Introduction 467 23.2 Modeling User Behavior 472 23.3 Grouping Users into Neighborhoods of Similarity 474 23.4 Similarity Metrics 481 23.5 Conclusion and Future Work 497 Appendix A Comparative Analysis of Comparison Algorithms 498 Appendix B Most Popular Searches 501 References 502 24. KNN Queries in Mobile Sensor Networks 507Wei-Guang Teng and Kun-Ta Chuang 24.1 Introduction 507 24.2 Preliminaries and Infrastructure-Based KNN Queries 509 24.3 Infrastructure-Free KNN Queries 511 24.4 Future Research Directions 519 24.5 Conclusions 519 References 520 25. Data Partitioning for Designing and Simulating Efficient Huge Databases 523Ladjel Bellatreche, Kamel Boukhalfa, Pascal Richard, and Soumia Benkrid 25.1 Introduction 523 25.2 Background and Related Work 527 25.3 Fragmentation Methodology 532 25.4 Hardness Study 535 25.5 Proposed Selection Algorithms 538 25.6 Impact of HP on Data Warehouse Physical Design 544 25.7 Experimental Studies 549 25.8 Physical Design Simulator Tool 553 25.9 Conclusion and Perspectives 559 References 560 26. Scalable Runtime Environments for Large-Scale Parallel Applications 563Camille Coti and Franck Cappello 26.1 Introduction 563 26.2 Goals of a Runtime Environment 565 26.3 Communication Infrastructure 567 26.4 Application Deployment 571 26.5 Fault Tolerance and Robustness 577 26.6 Case Studies 582 26.7 Conclusion 586 References 587 27. Increasing Performance through Optimization on APU 591Matthew Doerksen, Parimala Thulasiraman, and Ruppa Thulasiram 27.1 Introduction 591 27.2 Heterogeneous Architectures 591 27.3 Related Work 597 27.4 OpenCL, CUDA of the Future 600 27.5 Simple Introduction to OpenCL Programming 604 27.6 Performance and Optimization Summary 607 27.7 Application 607 27.8 Summary 609 Appendix 609 References 612 28. Toward Optimizing Cloud Computing: An Example of Optimization under Uncertainty 613Vladik Kreinovich 28.1 Cloud Computing: Why We Need It and How We Can Make It Most Efficient 613 28.2 Optimal Server Placement Problem: First Approximation 614 28.3 Server Placement in Cloud Computing: Toward a More Realistic Model 618 28.4 Predicting Cloud Growth: Formulation of the Problem and Our Approach to Solving This Problem 620 28.5 Predicting Cloud Growth: First Approximation 621 28.6 Predicting Cloud Growth: Second Approximation 622 28.7 Predicting Cloud Growth: Third Approximation 623 28.8 Conclusions and Future Work 625 Acknowledgments 625 Appendix: Description of Expenses Related to Cloud Computing 626 References 626 29. Modeling of Scalable Embedded Systems 629Arslan Munir, Sanjay Ranka, and Ann Gordon-Ross 29.1 Introduction 629 29.2 Embedded System Applications 631 29.3 Embedded Systems: Hardware and Software 634 29.4 Modeling: An Integral Part of the Embedded System Design Flow 638 29.5 Single- and Multiunit Embedded System Modeling 644 29.6 Conclusions 654 Acknowledgments 655 References 655 30. Scalable Service Composition in Pervasive Computing 659Joanna Siebert and Jiannong Cao 30.1 Introduction 659 30.2 Service Composition Framework 660 30.3 Approaches and Techniques for Scalable Service Composition in PvCE 664 30.4 Conclusions 671 References 671 31. Virtualization Techniques for Graphics Processing Units 675Pavan Balaji, Qian Zhu, and Wu-Chun Feng 31.1 Introduction 675 31.2 Background 677 31.3 VOCL Framework 677 31.4 VOCL Optimizations 682 31.5 Experimental Evaluation 687 31.6 Related Work 696 31.7 Concluding Remarks 696 References 697 32. Dense Linear Algebra on Distributed Heterogeneous Hardware with a Symbolic DAG Approach 699George Bosilca, Aurelien Bouteiller, Anthony Danalis, Thomas Herault, Piotr Luszczek, and Jack J. Dongara 32.1 Introduction and Motivation 699 32.2 Distributed Datafl ow by Symbolic Evaluation 701 32.3 The DAGuE Datafl ow Runtime 705 32.4 Datafl ow Representation 709 32.5 Programming Linear Algebra with DAGuE 716 32.6 Performance Evaluation 728 32.7 Conclusion 731 32.8 Summary 732 References 733 33. Fault-Tolerance Techniques for Scalable Computing 737Pavan Balaji, Darius Buntinas, and Dries Kimpe 33.1 Introduction and Trends in Large-Scale Computing Systems 737 33.2 Hardware Features for Resilience 738 33.3 Systems Software Features for Resilience 743 33.4 Application or Domain-Specifi c Fault-Tolerance Techniques 748 33.5 Summary 753 References 753 34. Parallel Programming Models for Scalable Computing 759James Dinan and Pavan Balaji 34.1 Introduction to Parallel Programming Models 759 34.2 The Message-Passing Interface (MPI) 761 34.3 Partitioned Global Address Space (PGAS) Models 765 34.4 Task-Parallel Programming Models 769 34.5 High-Productivity Parallel Programming Models 772 34.6 Summary and Concluding Remarks 775 Acknowledgment 775 References 775 35. Grid Simulation Tools for Job Scheduling and Data File Replication 777Javid Taheri, Albert Y. Zomaya, and Samee U. Khan 35.1 Introduction 777 35.2 Simulation Platforms 779 35.3 Problem Statement: Data-Aware Job Scheduling (DAJS) 792 References 795 Index 799

Reviews the latest advances in the all-important field of scalable computing In 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. This 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, Scalable Computing and Communications covers: Circuit and component designOperating systemsGreen computingNetwork-on-chip paradigmsComputational gridsHigh-performance computingSoftwareNetworking in scalable computing and mobile computingNext-generation networkingCloud computingPeer-to-peer systems Scalable Computing and Communications 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.