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E-raamat: Large Scale Network-Centric Distributed Systems

Edited by (Sharif University of Technology and Institute for Research in Fundamental Sciences (IPM), Tehran, Iran), Edited by (School of Information Technologies, The University of Sydney, Sydney, Australia)
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A highly accessible reference offering a broad range of topics and insights on large scale network-centric distributed systems

Evolving from the fields of high-performance computing and networking, large scale network-centric distributed systems continues to grow as one of the most important topics in computing and communication and many interdisciplinary areas. Dealing with both wired and wireless networks, this book focuses on the design and performance issues of such systems.

Large Scale Network-Centric Distributed Systems provides in-depth coverage ranging from ground-level hardware issues (such as buffer organization, router delay, and flow control) to the high-level issues immediately concerning application or system users (including parallel programming, middleware, and OS support for such computing systems). Arranged in five parts, it explains and analyzes complex topics to an unprecedented degree:





Part 1: Multicore and Many-Core (Mc) Systems-on-Chip Part 2: Pervasive/Ubiquitous Computing and Peer-to-Peer Systems Part 3: Wireless/Mobile Networks Part 4: Grid and Cloud Computing Part 5: Other Topics Related to Network-Centric Computing and Its Applications

Large Scale Network-Centric Distributed Systems is an incredibly useful resource for practitioners, postgraduate students, postdocs, and researchers.
Preface xix
Acknowledgments xxxvii
List of Figures
xxxix
List of Tables
li
List of Contributors
lv
PART 1 MULTICORE AND MANY-CORE (MC) SYSTEMS-ON-CHIP
1 A Reconfigurable On-Chip Interconnection Network for Large Multicore Systems
3(28)
Mehdi Modarressi
Hamid Sarbazi-Azad
1.1 Introduction
4(4)
1.1.1 Multicore and Many-Core Era
4(1)
1.1.2 On-Chip Communication
4(1)
1.1.3 Conventional Communication Mechanisms
4(1)
1.1.4 Network-on-Chip
5(1)
1.1.5 NoC Topology Customization
6(1)
1.1.6 NoCs and Topology Reconfigurations
6(1)
1.1.7 Reconfigurations Policy
7(1)
1.2 Topology and Reconfiguration
8(1)
1.3 The Proposed NoC Architecture
9(5)
1.3.1 Baseline Reconfigurable NoC
9(4)
1.3.2 Generalized Reconfigurable NoC
13(1)
1.4 Energy and Performance-Aware Mapping
14(5)
1.4.1 The Design Procedure for the Baseline Reconfigurable NoC
14(1)
1.4.1.1 Core-to-Network Mapping
15(1)
1.4.1.2 Topology and Route Generation
16(2)
1.4.2 Mapping and Topology Generation for Cluster-Based NoC
18(1)
1.5 Experimental Results
19(6)
1.5.1 Baseline Reconfigurable NoC
21(1)
1.5.2 Performance Evaluation with Cost Constraints
22(1)
1.5.3 Comparison Cluster-Based NoC
23(2)
1.6 Conclusion
25(6)
References
25(6)
2 Compilers, Techniques, and Tools for Supporting Programming Heterogeneous Many/Multicore Systems
31(22)
Pasquale Cantiello
Beniamino Di Martino
Francesco Moscato
2.1 Introduction
32(1)
2.2 Programming Models and Tools for Many/Multicore
32(10)
2.2.1 OpenMP
33(1)
2.2.2 Brook for GPUs
34(1)
2.2.3 Sh
35(1)
2.2.4 CUDA
36(1)
2.2.4.1 Memory Management
36(1)
2.2.4.2 Kernel Creation and Invocation
37(1)
2.2.4.3 Synchronization
38(1)
2.2.5 HMPP
38(1)
2.2.6 OpenCL
39(2)
2.2.7 OpenAcc
41(1)
2.3 Compilers and Support Tools
42(3)
2.3.1 RapidMind Multicore Development Platform
42(1)
2.3.2 OpenMPC
43(1)
2.3.3 Source-to-Source Transformers
43(1)
2.3.3.1 CHiLL
44(1)
2.3.3.2 Cetus
44(1)
2.3.3.3 ROSE Compiler
45(1)
2.3.3.4 LLVM
45(1)
2.4 CALuMET: A Tool for Supporting Software Parallelization
45(4)
2.4.1 Component-Based Source Code Analysis Architecture
45(2)
2.4.2 Algorithmic Recognizer Add-on
47(1)
2.4.3 Source Code Transformer for GPUs
48(1)
2.5 Conclusion
49(4)
References
50(3)
3 A Multithreaded Branch-and-Bound Algorithm for Solving The Flow-Shop Problem on a Multicore Environment
53(20)
Mohand Mezmaz
Nouredine Melab
Daniel Tuyttens
3.1 Introduction
54(1)
3.2 Flow-Shop Scheduling Problem
55(1)
3.3 Parallel Branch-and-Bound Algorithms
56(2)
3.3.1 Multiparametric Parallel Model
56(1)
3.3.2 Parallel Tree Exploration Model
57(1)
3.3.3 Parallel Evaluation of Bounds
57(1)
3.3.4 Parallel Evaluation of a Bound Model
58(1)
3.4 A Multithreaded Branch-and-Bound
58(2)
3.4.1 Low-Level Multithreaded B&B
58(1)
3.4.2 High-Level Multithreaded B&B
59(1)
3.5 The Proposed Multithreaded B&B
60(3)
3.6 Experiments and Results
63(5)
3.6.1 Flow-Shop Instances
63(1)
3.6.2 Hardware and Software Testbed
64(1)
3.6.3 Experimental Protocol
64(1)
3.6.4 Performance Analysis
65(1)
3.6.5 Page Faults
66(2)
3.6.6 Context Switches
68(1)
3.7 Conclusion
68(5)
References
70(3)
PART 2 PERVASIVE/UBIQUITOUS COMPUTING AND PEER-TO-PEER SYSTEMS
4 Large-Scale P2P-Inspired Problem-Solving: A Formal and Experimental Study
73(30)
Mathieu Djamai
Bilel Derbel
Nouredine Melab
4.1 Introduction
74(3)
4.1.1 Motivations
74(1)
4.1.2 Contribution and Results
75(1)
4.1.3 Related Works
76(1)
4.1.4 Outline
77(1)
4.2 Background
77(3)
4.3 A Pure Peer-to-Peer B&B Approach
80(7)
4.3.1 Preliminaries
80(1)
4.3.2 Information Sharing and Work Distribution
81(1)
4.3.2.1 Best Solution Sharing Mechanism
82(1)
4.3.2.2 Work Sharing
82(1)
4.3.2.3 Load Balancing
83(1)
4.3.3 Distributed Termination Detection
83(1)
4.3.3.1 Basic Observations
83(2)
4.3.3.2 Technical Details Sketch
85(1)
4.3.4 Asynchrony Issues
86(1)
4.4 Complexity Issues
87(3)
4.5 Experimental Results
90(9)
4.5.1 Experimental Testbed
90(2)
4.5.2 Experiments on Large-Scale Networks
92(1)
4.5.2.1 Parallel Efficiency
92(1)
4.5.2.2 Network Congestion and Simulation Scenarios
93(2)
4.5.2.3 Message Overhead
95(1)
4.5.2.4 Search Space Exploration Speed-Up
96(1)
4.5.2.5 Combinatorial Speed-Up
97(1)
4.5.3 Lower Scales' Results
98(1)
4.6 Conclusion
99(4)
Acknowledgment
99(1)
References
100(3)
5 Data Distribution Management
103(20)
Azzedine Boukerche
Yunfeng Gu
5.1 Addressing DDM in Different Network Environments
104(2)
5.2 DDM in P2P Overlay Networks
106(5)
5.2.1 Background
106(2)
5.2.2 Data Space Partitioning and Mapping
108(1)
5.2.3 Corresponding Overlay Network Support
109(2)
5.3 DDM in Cluster-Based Network Environments
111(12)
5.3.1 Basic Concepts in DDM
111(1)
5.3.1.1 Routing Space
112(1)
5.3.1.2 Objects, Federates, and Federation
112(1)
5.3.1.3 Behavior of Entities
112(1)
5.3.1.4 Other Important Concepts
113(1)
5.3.1.5 Performance of DDM Implementations
114(1)
5.3.2 Background
115(1)
5.3.3 Data Distribution Management Schemes
115(1)
5.3.3.1 Region-Based DDM Approach
115(1)
5.3.3.2 Grid-Based DDM Approaches
116(1)
5.3.3.3 Other DDM Schemes
117(1)
References
118(5)
6 Middleware Support for Context Handling and Integration in Ubiquitous Computing
123(24)
Frederico Lopes
Paulo F. Pires
Flavia C. Delicato
Thais Batista
Luci Pirmez
6.1 Introduction
124(2)
6.2 Ubiquitous Computing
126(2)
6.3 Middleware for Ubiquitous Computing
128(5)
6.3.1 Approaches and Techniques
128(2)
6.3.2 Existing Middleware Platforms
130(3)
6.4 A Solution to Integrating Context Provision Middleware for Ubiquitous Computing
133(9)
6.4.1 Overview
133(1)
6.4.2 Terminology
134(1)
6.4.2.1 Services
134(1)
6.4.2.2 Semantic Workflow
134(1)
6.4.2.3 Execution Plan
135(1)
6.4.3 Context Model
135(1)
6.4.4 Architecture
136(3)
6.4.5 Service Metadata
139(1)
6.4.6 Workflow Specification
140(1)
6.4.7 Execution Plan Selection
140(2)
6.5 Conclusion
142(5)
Acknowledgments
142(1)
References
143(4)
PART 3 WIRELESS/MOBILE NETWORKS
7 Challenges in The Use of Wireless Sensor Networks For Monitoring The Health of Civil Structures
147(20)
Flavia C. Delicato
Igor L. dos Santos
Luci Pirmez
Paulo F. Pires
Claudio M. de Farias
7.1 Introduction
148(2)
7.2 Structural Health Monitoring
150(5)
7.2.1 The Concept of Structural Health Monitoring
150(2)
7.2.2 Requirements of Modal-Based Techniques in SHM Solutions
152(1)
7.2.3 The Generations of Sensor Networks for SHM
153(2)
7.3 Wireless Sensor Networks
155(2)
7.4 Applying Wireless Sensor Networks for Structural Health Monitoring
157(6)
7.4.1 The Second Generation of Sensor Networks for SHM
157(1)
7.4.2 The Third Generation of Sensor Networks for SHM
158(3)
7.4.3 A Fully Decentralized, Network-Centric Approach: Sensor-SHM
161(2)
7.5 Conclusion
163(4)
Acknowledgments
164(1)
References
164(3)
8 Mobility Effects in Wireless Mobile Networks
167(16)
Abbas Nayebi
Hamid Sarbazi-Azad
8.1 Introduction
167(1)
8.2 The Effect of Node Mobility on Wireless Links
168(4)
8.2.1 Geometric Modeling
170(1)
8.2.2 LL and RLL Properties
171(1)
8.3 The Effect of Node Mobility on Network Topology
172(5)
8.3.1 Definitions of Connectivity
173(1)
8.3.2 Phase Transition Phenomenon in Connectivity and Disconnection Degree
174(3)
8.4 Conclusion
177(6)
References
178(5)
9 Analytical Model of Time-Critical Wireless Sensor Network: Theory And Evaluation
183(20)
Kambiz Mizanian
Amir Hossein Jahangir
9.1 Introduction
184(1)
9.2 Real-Time Wireless Sensor Network: An Overview
185(3)
9.2.1 Previous Work on a Related Analytical Model
185(1)
9.2.2 Previous Work on the Real-Time Communication Protocols
186(2)
9.3 Real-Time Degree
188(7)
9.3.1 Basic Assumptions
188(2)
9.3.2 Evaluation of the Real-Time Degree
190(5)
9.4 Reliable Real-Time Degree
195(2)
9.5 Model Validation
197(2)
9.6 Conclusion
199(4)
References
200(3)
10 Multicast Transport Protocols for Large-Scale Distributed Collaborative Environments
203(16)
Haifa Raja Maamar
Azzedine Boukerche
10.1 Introduction
204(1)
10.2 Definition and Features
204(3)
10.2.1 Definition
204(1)
10.2.2 Features
205(1)
10.2.2.1 Reliability
205(1)
10.2.2.2 Congestion and Flow Control
206(1)
10.2.2.3 Ordering
206(1)
10.2.2.4 Error Recovery
206(1)
10.2.2.5 Group Management
207(1)
10.3 Classification of Multicast Protocols
207(9)
10.3.1 General-Purpose Protocols
208(1)
10.3.1.1 Reliable Broadcast Protocol (RBP)
208(1)
10.3.1.2 Multicast Transport Protocol (MTP)
208(1)
10.3.1.3 Reliable Multicast Protocol (RMP)
209(1)
10.3.1.4 Xpress Transport Protocol (XTP)
209(1)
10.3.2 Multicast Interactive Applications
210(1)
10.3.2.1 Multicast Transport Protocol-2 (MTP-2)
210(1)
10.3.2.2 Real-Time Transport Protocol (RTP)
211(1)
10.3.2.3 Scalable Reliable Multicast (SRM)
211(1)
10.3.2.4 Reliable Adaptive Multicast Protocol (RAMP)
212(1)
10.3.3 Data Distribution Services
212(1)
10.3.3.1 Tree-Based Multicast Transport Protocol (TMTP)
212(1)
10.3.3.2 Reliable Multicast Transport Protocol (RMTP)
213(2)
10.3.3.3 Multicast File Transfer Protocol (MFTP)
215(1)
10.3.3.4 Tree-Based Reliable Multicast Protocol (TRAM)
215(1)
10.4 Conclusion
216(3)
References
216(3)
11 Nature-Inspired Computing For Autonomic Wireless Sensor Networks
219(38)
Wei Li
Javid Taheri
Albert Y. Zomaya
Franciszek Seredynski
Bjorn Landfeldt
11.1 Introduction
220(2)
11.2 Autonomic WSNs
222(2)
11.3 Principles of Nature-Inspired Computing
224(2)
11.4 Cellular Automata
226(2)
11.4.1 One-Dimensional Cellular Automata and Its Applications in WSNs
226(1)
11.4.2 Two-Dimensional Cellular Automata and Its Applications in WSNs
227(1)
11.5 Swarm Intelligence
228(5)
11.5.1 Ant Colony Optimization
229(1)
11.5.1.1 Basic Concepts
229(1)
11.5.1.2 Applications in WSNs
230(1)
11.5.2 Firefly Synchronization
231(1)
11.5.2.1 Applications in WSNs
231(1)
11.5.3 Particle Swarm Optimization
232(1)
11.5.3.1 Applications in WSNs
233(1)
11.6 Artificial Immune Systems
233(5)
11.6.1 Negative Selection
233(1)
11.6.2 Danger Theory
234(1)
11.6.3 Clonal Selection
234(1)
11.6.4 Immune Networks
235(1)
11.6.5 Applications in WSNs
235(1)
11.6.6 The Cognitive Immune Model
236(1)
11.6.6.1 Application in WSNs
237(1)
11.7 Evolutionary Computing
238(4)
11.7.1 Basic Concepts
238(1)
11.7.2 Genetic Algorithms
239(1)
11.7.3 Genetic Programming
239(1)
11.7.4 Evolution Strategies
240(1)
11.7.5 Evolutionary Programming
240(1)
11.7.6 Applications in WSNs
241(1)
11.8 Molecular Biology
242(1)
11.9 Bio-Networking Architecture
243(1)
11.10 Conclusion
244(13)
References
244(13)
PART 4 GRID AND CLOUD COMPUTING
12 Smart RPC-Based Computing in Grids and on Clouds
257(34)
Thomas Brady
Oleg Girko
Alexey Lastovetsky
12.1 Introduction
258(8)
12.1.1 GridRPC Programming Model and API
259(1)
12.1.1.1 Design of the GridRPC Programming Model
259(1)
12.1.1.2 GridRPC: API and Semantics
260(1)
12.1.2 GridRPC: A GridRPC Application
261(2)
12.1.3 Implementing the GridRPC Model in GridSolve
263(1)
12.1.3.1 GridSolve: Agent Discovery
263(1)
12.1.3.2 Run-time GridRPC Task Call
263(2)
12.1.4 GridRPC Limitations
265(1)
12.2 SmartGridRPC and SmartGridSolve
266(11)
12.2.1 SmartGridRPC Programming Model and API
266(1)
12.2.1.1 SmartGridRPC Programming Model
267(3)
12.2.1.2 SmartGridRPC: API and Semantics
270(2)
12.2.1.3 A SmartGridRPC Application
272(1)
12.2.2 SmartGridSolve: Implementing SmartGridRPC in GridSolve
272(2)
12.2.2.1 Agent Discovery
274(1)
12.2.2.2 Run-time of Client Application
274(2)
12.2.2.3 Fault Tolerance
276(1)
12.3 Making SmartGridSolve Smarter
277(5)
12.3.1 SmartGridSolve Approach to Smart Mapping and Its Limitations
277(2)
12.3.2 A Better Approach to Smart Mapping
279(1)
12.3.3 Better Approaches to Fault Tolerance
280(1)
12.3.3.1 Recovery from Task Failures
280(1)
12.3.3.2 Restarting Only Relevant Tasks
281(1)
12.3.3.3 Losing Fewer Results
281(1)
12.3.3.4 More Reliable Mapping
282(1)
12.4 Smart RPC-Based Computing on Clouds: Adaptation of SmartGridRPC and SmartGridSolve to Cloud Computing
282(9)
12.4.1 Cloud Computing
283(1)
12.4.1.1 Infrastructure as a Service
283(1)
12.4.1.2 Platform as a Service
283(1)
12.4.1.3 Software as a Service
284(1)
12.4.2 SmartCloudSolve (SCS)
284(1)
12.4.2.1 Overview
284(1)
12.4.2.2 Advantages of the SCS Platform
285(1)
12.4.2.3 High-Level Design of the SCS Platform
285(1)
12.4.2.4 SCS API and Application Implementation
286(2)
Acknowledgment
288(1)
References
288(3)
13 Profit-Maximizing Resource Allocation for Multitier Cloud Computing Systems Under Service Level Agreements
291(28)
Hadi Goudarzi
Massoud Pedram
13.1 Introduction
292(2)
13.2 Review of Datacenter Power Management Techniques
294(2)
13.3 Review of Datacenter Performance Management Techniques
296(2)
13.4 System Model of a Multitier Application Placement Problem
298(5)
13.4.1 Multitier Service Model
299(3)
13.4.2 SLA Model for This System
302(1)
13.4.3 Resource Management Problem
303(1)
13.5 Profit Maximization in a Hosting Datacenter
303(7)
13.5.1 Problem Formulation
303(2)
13.5.2 Initial Solution
305(3)
13.5.3 Resource Consolidation Using Force-Directed Search
308(2)
13.6 Simulation Results
310(4)
13.7 Conclusion
314(5)
References
314(5)
14 Market-Oriented Cloud Computing and The Cloudbus Toolkit
319(40)
Rajkumar Buyya
Suraj Pandey
Christian Vecchiola
14.1 Introduction
320(2)
14.2 Cloud Computing
322(16)
14.2.1 Cloud Definition and Market-Oriented Computing
323(2)
14.2.2 Cloud Computing Reference Model
325(1)
14.2.3 State of the Art in Cloud Computing
326(1)
14.2.3.1 Infrastructure as a Service
326(2)
14.2.3.2 Platform as a Service
328(1)
14.2.3.3 Software as a Service
329(1)
14.2.3.4 Alliances and Standardization Initiatives
330(1)
14.2.4 Open Challenges
331(1)
14.2.4.1 Virtualization
331(1)
14.2.4.2 Security, Privacy, and Trust
332(1)
14.2.4.3 Legal and Regulatory
333(1)
14.2.4.4 Service Level Agreements and Quality of Service
334(1)
14.2.4.5 Energy Efficiency
335(1)
14.2.4.6 Programming Environments and Application Development
335(1)
14.2.4.7 Applications on Clouds
336(1)
14.2.4.8 Standardization
337(1)
14.3 Cloudbus: Vision and Architecture
338(2)
14.4 Cloudbus and Clouds Lab Technologies
340(5)
14.4.1 Aneka
340(1)
14.4.2 Brokers: Harnessing Cloud and Other Distributed Resources
341(1)
14.4.3 Workflow Engine
342(1)
14.4.4 Market Maker/Meta-broker
343(1)
14.4.5 InterCloud
343(1)
14.4.6 MetaCDN
343(1)
14.4.7 Data Center Optimization
344(1)
14.4.8 Energy-Efficient Computing
344(1)
14.4.9 CloudSim
344(1)
14.5 Experimental Results
345(5)
14.5.1 Aneka Experiment: Application Deadline-Driven Provisioning of Cloud Resources
345(1)
14.5.2 Broker Experiment: Scheduling on Cloud and Other Distributed Resources
346(3)
14.5.3 Deploying ECG Analysis Applications in Cloud Using-Aneka
349(1)
14.6 Related Technologies, Integration, and Deployment
350(1)
14.7 Conclusion
351(8)
Acknowledgments
353(1)
References
354(5)
15 A Cloud Broker Architecture for Multicloud Environments
359(18)
Jose Luis Lucas-Simarro
Inigo San Aniceto
Rafael Moreno-Vozmediano
Ruben S. Montero
Ignacio M. Llorente
15.1 Introduction
360(1)
15.2 State of the Art on Cloud Brokering
361(2)
15.3 Challenges of Cloud Brokering
363(1)
15.4 Proposal of a Broker Architecture for Multicloud Environments
364(3)
15.4.1 Broker Components
365(1)
15.4.1.1 Database
365(1)
15.4.1.2 Scheduler
366(1)
15.4.1.3 VM Manager
366(1)
15.4.1.4 Cloud Manager
366(1)
15.4.2 Service Description
366(1)
15.4.2.1 Service Components and Lifetime
366(1)
15.4.2.2 Scheduling Parameters
367(1)
15.4.2.3 Cloud Instance Usage and Instance Performance
367(1)
15.5 Scheduling Policies for Efficient Cloud Brokering
367(2)
15.5.1 Static vs. Dynamic Scheduling
367(1)
15.5.2 Optimization Criteria
367(1)
15.5.3 User Restrictions
368(1)
15.6 Results
369(4)
15.6.1 Cost Optimization in a Single Cloud
369(1)
15.6.2 Cost Optimization in a Multicloud Environment
370(1)
15.6.2.1 Infrastructure for a Virtual Classroom
370(1)
15.6.2.2 Infrastructure for a Web Server
371(2)
15.7 Conclusion
373(4)
Acknowledgments
374(1)
References
375(2)
16 Energy-Efficient Resource Utilization in Cloud Computing
377(32)
Giorgio L. Valentini
Samee U. Khan
Pascal Bouvry
16.1 Introduction
378(2)
16.2 Related Work
380(1)
16.3 Energy-Efficient Utilization of Resources in Cloud Computing Systems
381(5)
16.3.1 Cloud Computing
381(1)
16.3.2 Energy Model
382(1)
16.3.3 Task Consolidation Problem
382(1)
16.3.4 Task Consolidation Algorithm
383(1)
16.3.4.1 Overview
383(1)
16.3.4.2 Cost Functions (ECTC and Max Util)
383(1)
16.3.4.3 Task Consolidation Algorithm
384(1)
16.3.5 Application of the Model: A Working Example
384(2)
16.4 Complementarity Approach
386(9)
16.4.1 Main Idea
386(1)
16.4.2 Motivation
387(1)
16.4.3 Approach
387(1)
16.4.4 Metric Normalization
388(1)
16.4.5 Evaluation Space
389(1)
16.4.6 Selection of the Best Candidate
389(1)
16.4.6.1 Mathematical Model
389(1)
16.4.6.2 Algorithm
390(1)
16.4.7 Processing of Equivalent Solutions
391(1)
16.4.8 Energy-Efficient Task Consolidation Algorithm
391(1)
16.4.9 An Intuitive Example
391(4)
16.5 Simulation Results
395(7)
16.5.1 Simulation Setup
395(2)
16.5.2 Results
397(1)
16.5.2.1 Energy Efficiency
397(5)
16.5.2.2 Speed Analysis
402(1)
16.6 Discussion of Results
402(2)
16.7 Conclusion
404(5)
References
405(4)
17 Semantics-Based Resource Discovery in Large-Scale Grids
409(22)
Juan Li
Samee U. Khan
Nasir Ghani
17.1 Introduction
410(1)
17.2 Related Work
411(1)
17.3 Virtual Organization Formation
412(5)
17.3.1 Overview
412(1)
17.3.2 Ontological Directories
412(3)
17.3.3 Ontology Directory Lookup and VO Register
415(1)
17.3.3.1 Exact Lookups
415(1)
17.3.3.2 Browser-Based Lookups
415(1)
17.3.3.3 Keyword-Based Lookups
416(1)
17.3.3.4 VO Register
416(1)
17.3.3.5 Directory Overlay Maintenance
417(1)
17.4 Semantics-Based Resource Discovery in Virtual Organizations
417(4)
17.4.1 Semantic Similarity
418(1)
17.4.2 Illustrative Example
419(2)
17.4.3 Semantics-Based Topology Adaptation and Search
421(1)
17.5 Prototype Implementation and Evaluation
421(6)
17.5.1 Implementation
421(3)
17.5.2 GONID Toolkit Deployment and Evaluation
424(1)
17.5.3 Evaluation Based on Simulation
425(2)
17.6 Conclusion
427(4)
References
427(4)
18 Game-Based Models of Grid User's Decisions in Security-Aware Scheduling
431(32)
Joanna Kolodziej
Samee U. Khan
Lizhe Wang
Dan Chen
18.1 Introduction
432(1)
18.2 Security-Aware Scheduling Problems in Computational Grids
433(8)
18.2.1 Generic Model of Secure Grid Clusters
434(3)
18.2.2 Security Criterion in Grid Scheduling
437(3)
18.2.2.1 Risky Mode
440(1)
18.2.3 Requirements of Grid End Users for Scheduling
440(1)
18.3 Game Models in Security-Aware Grid Scheduling
441(6)
18.3.1 Symmetric and Asymmetric Non-cooperative Games of the End Users
442(1)
18.3.1.1 Non-cooperative Symmetric Game
443(1)
18.3.1.2 Asymmetric Scenario---Stackelberg Game
444(2)
18.3.2 Cooperative and Semi-cooperative Game Scenarios
446(1)
18.3.3 Online Scheduling Games
446(1)
18.4 Case Study: Approximating the Equilibrium States of the End Users' Symmetric Game Using the Genetic Metaheuristics
447(13)
18.4.1 Specification of the Game
448(1)
18.4.1.1 Characteristics of Game Players and Decision Variables
448(1)
18.4.1.2 Solving the Grid Users' Game
448(1)
18.4.1.3 Game Cost Functions
449(1)
18.4.1.4 Task Execution Cost
450(1)
18.4.1.5 Resource Utilization Cost
450(1)
18.4.1.6 Security-Assurance Cost
451(1)
18.4.2 Genetic-Based Resolution Methods for Game Models
452(1)
18.4.2.1 Schedulers Implemented in Global Module
452(1)
18.4.2.2 Local Schedulers in Players' Module
453(1)
18.4.3 Empirical Setup
454(2)
18.4.3.1 Performance Measures
456(1)
18.4.4 Results
457(3)
18.5 Conclusion
460(3)
References
460(3)
19 Addressing Open Issues on Performance Evaluation in Cloud Computing
463(22)
Beniamino Di Martino
Massimo Ficco
Massimiliano Rak
Salvatore Venticinque
19.1 Introduction
464(1)
19.2 Benchmarking Approaches
465(3)
19.2.1 HPC-Like Benchmarking
465(1)
19.2.2 Benchmark Standards
466(1)
19.2.3 Cloud-Oriented Benchmarks
466(1)
19.2.4 Benchmark as a Service Approach
467(1)
19.2.5 Considerations
468(1)
19.3 Monitoring in Cloud Computing
468(6)
19.3.1 What Should Be Monitored
468(1)
19.3.1.1 Infrastructure as a Service (IaaS)
469(1)
19.3.1.2 Platform as a Service (PaaS)
470(1)
19.3.1.3 Service as a Service (SaaS)
470(2)
19.3.2 How to Monitor
472(1)
19.3.2.1 Supporting Tools
473(1)
19.4 Attack Countermeasures in Cloud Computing
474(6)
19.5 Conclusion
480(5)
References
480(5)
20 Broker-Mediated Cloud-Aggregation Mechanism Using Markovian Queues For Scheduling Bag-of-Tasks (BOT) Applications
485(18)
Ganesh Neelakanta Iyer
Bharadwaj Veeravalli
20.1 Introduction
486(1)
20.2 Literature Review and Contributions
487(1)
20.2.1 Literature Review
487(1)
20.2.2 Contributions and Scope of This
Chapter
488(1)
20.3 Problem Setting and Notations
488(1)
20.4 Proposed Cloud Aggregation Mechanism
489(5)
20.4.1 Task Distribution to Minimize the Application Completion Time
490(1)
20.4.1.1 Integer Approximation Techniques
491(1)
20.4.1.2 Eliminating CSPs with Lower Resource Capabilities
492(1)
20.4.2 Task Distribution Based on Budget Requirements
493(1)
20.5 Performance Evaluation and Discussions
494(3)
20.5.1 Analysis of Task Execution Time vs. Budget Requirements
495(1)
20.5.2 Analysis of the Total User Expenditure vs. Budget Requirements
496(1)
20.5.3 Analysis of Task Distribution Based on Budget Requirements
496(1)
20.6 Discussions
497(1)
20.6.1 Applicability of Our Model to Divisible Load Applications
497(1)
20.6.2 Flexibility in Considering More User Requirements
497(1)
20.6.3 Consideration of Cloud Characteristics
498(1)
20.7 Conclusion
498(5)
References
499(4)
21 On The Design of a Budget-Conscious Adaptive Scheduler for Handling Large-Scale Many-Task Workflow Applications in Clouds
503(24)
Bharadwaj Veeravalli
Lingfang Zeng
Xiaorong Li
21.1 Introduction
504(1)
21.2 Related Work and Motivation
505(1)
21.3 System Model and Problem Setting
506(6)
21.3.1 System Model
506(3)
21.3.2 Many-Task Workflow Scheduling Problem
509(3)
21.4 Proposed Scheduling Algorithm
512(4)
21.4.1 Static ScaleStar
512(1)
21.4.1.1 Initial Assignment Phase
513(1)
21.4.1.2 Task Reassignment Phase
513(1)
21.4.1.3 DeSlack Policy
513(1)
21.4.2 Dynamic Adaptive Strategy
514(2)
21.5 Performance Evaluation and Results
516(6)
21.5.1 Evaluation Methodology
516(1)
21.5.2 Real-World MTW Applications
517(2)
21.5.3 Synthetic MTWs
519(1)
21.5.4 Static Strategy Evaluation
519(2)
21.5.5 Dynamic Strategy Evaluation
521(1)
21.6 Conclusion
522(5)
References
523(4)
22 Virtualized Environment Issues in The Context of a Scientific Private Cloud
527(24)
Bruno Schulze
Henrique de Medeiros Kloh
Matheus Bousquet Bandini
Antonio Roberto Mury
Daniel Massami Muniz Yokoyama
Victor Dias de Oliveira
Fabio Andre Machado Porto
Giacomo Victor McEvoy Valenzano
22.1 Introduction
528(1)
22.2 Related Works
528(3)
22.3 Methodology
531(2)
22.3.1 Experiments and Objectives
531(1)
22.3.2 Experimental Infrastructure
531(2)
22.4 Experiments
533(11)
22.4.1 Experiment 1: Influence of the Hypervisors on Performance
533(2)
22.4.2 Experiment 2: Hybrid Virtualized Environment Evaluation
535(4)
22.4.3 Experiment 3: Virtualized Database
539(4)
22.4.4 Result Analysis
543(1)
22.5 Conclusion
544(2)
22.6 Glossary
546(5)
Acknowledgments
547(1)
References
547(4)
PART 5 OTHER TOPICS RELATED TO NETWORK-CENTRIC COMPUTING AND ITS APPLICATIONS
23 In-Advance Bandwidth Scheduling in e-Science Networks
551(40)
Yan Li
Eunsung Jung
Sanjay Ranka
Nageswara S. Rao
Sartaj Sahni
23.1 Introduction
552(2)
23.2 Temporal Network Model
554(2)
23.2.1 Slotted Time
554(1)
23.2.2 Continuous Time
555(1)
23.3 Single-Path Scheduling
556(14)
23.3.1 Problem Definitions
556(1)
23.3.2 Path Computation Algorithms
557(1)
23.3.2.1 Fixed Slot
557(3)
23.3.2.2 Maximum Bandwidth in a Slot
560(1)
23.3.2.3 Maximum Duration
560(1)
23.3.2.4 First Slot
561(1)
23.3.2.5 All Slots
562(1)
23.3.2.6 All Pairs, All Slots
562(1)
23.3.3 Performance Metrics
562(1)
23.3.3.1 Space Complexity
563(1)
23.3.3.2 Time Complexity
563(1)
23.3.3.3 Effectiveness
564(2)
23.3.4 Experiments
566(4)
23.4 Multiple-Path Scheduling
570(17)
23.4.1 Problem Definition
570(1)
23.4.1.1 Data Structures
571(1)
23.4.2 Optimal Solution and N-Batch Heuristics
572(2)
23.4.2.1 N-Batch Heuristics
574(1)
23.4.3 Online Scheduling Algorithms
575(1)
23.4.3.1 Greedy Algorithm
575(1)
23.4.3.2 Greedy Scheduling with Finish Time Extension (GOS-E)
576(2)
23.4.3.3 K-Path Algorithms
578(1)
23.4.4 Experimental Evaluation
578(1)
23.4.4.1 Experimental Framework
578(1)
23.4.4.2 Single Start Time Scheduling (SSTS)
579(3)
23.4.4.3 Multiple Start Time Scheduling (MSTS)
582(3)
23.4.4.4 GOS vs. GOS-E
585(2)
23.5 Conclusion
587(4)
Acknowledgment
587(1)
References
587(4)
24 Routing and Wavelength Assignment in Optical Networks
591(28)
Yan Li
Sanjay Ranka
Sartaj Sahni
24.1 Introduction
592(1)
24.2 Scheduling in Full-Wavelength Conversion Network
593(10)
24.2.1 Problem Definition
593(1)
24.2.2 Routing Algorithms
594(1)
24.2.2.1 Modified Switch Path First Algorithm (MSPF)
595(1)
24.2.2.2 Modified Switch Window First Algorithm (MSWF)
595(1)
24.2.3 Wavelength Assignment Algorithms
596(2)
24.2.4 Performance Evaluation
598(1)
24.2.5 Experiments
599(1)
24.2.5.1 Simulation Environment
599(1)
24.2.5.2 Evaluated Algorithms
599(1)
24.2.5.3 Results and Observations
600(2)
24.2.6 Conclusions
602(1)
24.3 Scheduling in Sparse Wavelength Conversion Network
603(16)
24.3.1 Problem Description
603(1)
24.3.2 Extended Network Model
604(1)
24.3.3 Routing and Wavelength Assignment Algorithms
605(1)
24.3.3.1 Extended Bellman-Ford Algorithm for Sparse Wavelength Conversion
605(1)
24.3.3.2 k-Alternative Path Algorithm
606(1)
24.3.3.3 Breaking the Ties in Path Selection
606(1)
24.3.3.4 Wavelength Assignment
607(1)
24.3.4 Experimental Evaluation
608(1)
24.3.4.1 Experimental Framework
608(1)
24.3.4.2 Slack Tie-Breaking Scheme
609(2)
24.3.4.3 Blocking Probability
611(2)
24.3.4.4 Requests' Average Start Time
613(1)
24.3.4.5 Scheduling Overhead
614(1)
24.3.4.6 Algorithm Switching Strategy
615(1)
24.3.5 Conclusions
616(1)
Acknowledgment
617(1)
References
617(2)
25 Computational Graph Analytics for Massive Streaming Data
619(30)
David Ediger
Jason Riedy
David A. Bader
Henning Meyerhenke
25.1 Introduction
620(2)
25.2 STINGER: A General-Purpose Data Structure for Dynamic Graphs
622(3)
25.2.1 Related Graph Data Structures
622(1)
25.2.2 The STINGER Data Structure
622(2)
25.2.3 Finding Parallelism in Streams and Analytics
624(1)
25.3 Algorithm for Updating Clustering Coefficients
625(3)
25.3.1 Generic Algorithm
625(1)
25.3.2 Approximating Clustering Coefficients Using a Bloom Filter
626(2)
25.4 Tracking Connected Components in Scale-Free Graphs
628(4)
25.4.1 Problem Structure
628(1)
25.4.2 The Algorithm in Detail
629(2)
25.4.3 Discussion
631(1)
25.5 Implementation
632(2)
25.5.1 Multithreaded Platforms
632(1)
25.5.2 The STINGER Data Structure
632(1)
25.5.3 Multithreaded Implementation of Algorithm 25.1 (Clustering Coefficients)
633(1)
25.5.4 Multithreaded Implementation of Algorithm 25.2 (Connected Components)
634(1)
25.6 Experimental Results
634(9)
25.6.1 Clustering Coefficient Experiments
634(1)
25.6.1.1 Scalability of the Initial Computation
635(1)
25.6.1.2 Number of Individual Updates per Second
635(2)
25.6.2 Connected Components
637(6)
25.7 Related Work
643(1)
25.7.1 Streaming Data
643(1)
25.7.2 Graph Data Structures
643(1)
25.7.3 Tracking Connected Components
643(1)
25.8 Conclusion
644(5)
Acknowledgments
645(1)
References
645(4)
26 Knowledge Management for Fault-Tolerant Water Distribution
649(30)
Jing Lin
Ali Hurson
Sahra Sedigh
26.1 Introduction
650(2)
26.2 Related Work
652(1)
26.3 Agent-Based Model for WDN Operation
653(3)
26.4 Classes in WDN Ontology Framework
656(3)
26.4.1 WDN Ontology Class
656(2)
26.4.2 Automatic Reasoning Based on Classes
658(1)
26.5 Automated Failure Classification and Mitigation
659(9)
26.5.1 Object Properties for Behavior Reasoning
659(7)
26.5.2 Data Properties for Value Reasoning
666(2)
26.6 Validation of Automated Failure Mitigation
668(6)
26.6.1 Initial Configuration and Normal Operation
668(2)
26.6.2 Failure Scenario and Automated Mitigation
670(4)
26.7 Conclusion
674(5)
Acknowledgment
675(1)
References
675(4)
Index 679
HAMID SARBAZI-AZAD, PhD, is Professor of Computer Engineering at Sharif University of Technology and heads the School of Computer Science at the Institute for Research in Fundamental Sciences (IPM) in Tehran, Iran. His research interests include high-performance computing architectures and networks, SoC and NoCs, and memory/storage systems. He has been the editor-in-chief of the CSI Journal on Computer Science & Engineering, and associate editor/editor/guest editor of several related journals including IEEE Transactions on Computers. He has received the Khwarizmi International Award and the TWAS Young Scientist Award in 2007.

ALBERT Y. ZOMAYA, PhD, is the Chair Professor of High Performance Computing & Networking in the School of Information Technologies at The University of Sydney. He is also the Director of the Centre for Distributed and High Performance Computing. Professor Zomaya is the author/coauthor of seven books, more than 450 publications in technical journals and conference proceedings, and the editor of fourteen books and nineteen conference volumes. He is a Fellow of the AAAS, IEEE, and IET.
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
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