book

Large Scale Network-Centric Distributed Systems

by Hamid Sarbazi-Azad, Albert Y. Zomaya

October 2013

Intermediate to advanced

760 pages

23h 42m

English

Wiley-IEEE Press

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Cover
Wiley Series on Parallel and Distributed Computing
Title Page
Copyright
Dedication
Preface
Acknowledgments
List of Figures
List of Tables
List of Contributors

Part 1: Multicore and Many-Core (MC) Systems-On-Chip
Chapter 1: A Reconfigurable On-Chip Interconnection Network for Large Multicore Systems
1.1 Introduction1.2 Topology and Reconfiguration1.3 The proposed NoC Architecture1.4 Energy and Performance-Aware Mapping1.5 Experimental Results1.6 ConclusionReferences
Chapter 2: Compilers, Techniques, and Tools for Supporting Programming Heterogeneous Many/Multicore Systems
2.1 Introduction2.2 Programming Models and Tools for Many/Multicore2.3 Compilers and Support Tools2.4 CALuMET: A Tool for Supporting Software Parallelization2.5 ConclusionReferences
Chapter 3: A Multithreaded Branch-and-Bound Algorithm for Solving the Flow-Shop Problem on a Multicore Environment
3.1 Introduction3.2 Flow-Shop Scheduling Problem3.3 Parallel Branch-and-Bound Algorithms3.4 A Multithreaded Branch-and-Bound3.5 The Proposed Multithreaded B&B3.6 Experiments and Results3.7 ConclusionReferences
Part 2: Pervasive/Ubiquitous Computing and Peer-To-Peer Systems
Chapter 4: Large-Scale P2P-Inspired Problem-Solving: A Formal and Experimental Study
4.1 Introduction4.2 Background4.3 A Pure Peer-to-Peer B&B Approach4.4 Complexity Issues4.5 Experimental Results4.6 ConclusionAcknowledgmentReferences
Chapter 5: Data Distribution Management
5.1 Addressing DDM in Different Network Environments5.2 DDM in P2P Overlay Networks5.3 DDM in Cluster-Based Network EnvironmentsReferences
Chapter 6: Middleware Support for Context Handling and Integration in Ubiquitous Computing
6.1 Introduction6.2 Ubiquitous Computing6.3 Middleware for Ubiquitous Computing6.4 A Solution to Integrating Context Provision Middleware for Ubiquitous Computing6.5 ConclusionAcknowledgmentsReferences
Part 3: Wireless/Mobile Networks
Chapter 7: Challenges in the Use of Wireless Sensor Networks for Monitoring the Health of Civil Structures
7.1 Introduction7.2 Structural Health Monitoring7.3 Wireless Sensor Networks7.4 Applying Wireless Sensor Networks for Structural Health Monitoring7.5 ConclusionAcknowledgmentsReferences
Chapter 8: Mobility Effects in Wireless Mobile Networks
8.1 Introduction8.2 The Effect of Node Mobility on Wireless Links8.3 The Effect of Node Mobility on Network Topology8.4 ConclusionReferences
Chapter 9: Analytical Model of Time-Critical Wireless Sensor Network: Theory and Evaluation
9.1 Introduction9.2 Real-Time Wireless Sensor Network: An Overview9.3 Real-Time Degree9.4 Reliable Real-Time Degree9.5 Model Validation9.6 ConclusionReferences
Chapter 10: Multicast Transport Protocols for Large-Scale Distributed Collaborative Environments
10.1 Introduction10.2 Definition and Features10.3 Classification of Multicast Protocols10.4 ConclusionReferences
Chapter 11: Nature-Inspired Computing for Autonomic Wireless Sensor Networks
11.1 Introduction11.2 Autonomic WSNs11.3 Principles of Nature-Inspired Computing11.4 Cellular Automata11.5 Swarm Intelligence11.6 Artificial Immune Systems11.7 Evolutionary Computing11.8 Molecular Biology11.9 Bio-networking Architecture11.10 ConclusionReferences
Part 4: Grid and Cloud Computing
Chapter 12: Smart RPC-Based Computing in Grids and on Clouds
12.1 Introduction12.2 SmartGridRPC and SmartGridSolve12.3 Making SmartGridSolve Smarter12.4 Smart RPC-Based Computing on Clouds: Adaptation of SmartGridRPC and SmartGridSolve to Cloud ComputingAcknowledgmentReferences
Chapter 13: Profit-Maximizing Resource Allocation for Multitier Cloud Computing Systems under Service Level Agreements
13.1 Introduction13.2 Review of Datacenter Power Management Techniques13.3 Review of Datacenter Performance Management Techniques13.4 System Model of a Multitier Application Placement Problem13.5 Profit Maximization in a Hosting Datacenter13.6 Simulation Results13.7 ConclusionReferences
Chapter 14: Market-Oriented Cloud Computing and the Cloudbus Toolkit
14.1 Introduction14.2 Cloud Computing14.3 Cloudbus: Vision and Architecture14.4 Cloudbus and Clouds Lab Technologies14.5 Experimental Results14.6 Related Technologies, Integration, and Deployment14.7 ConclusionAcknowledgmentsReferences
Chapter 15: A Cloud Broker Architecture for MultiCloud Environments
15.1 Introduction15.2 State of the Art on Cloud Brokering15.3 Challenges of Cloud Brokering15.4 Proposal of a Broker Architecture for Multicloud Environments15.5 Scheduling Policies for Efficient Cloud Brokering15.6 Results15.7 ConclusionAcknowledgmentsReferences
Chapter 16: Energy-Efficient Resource Utilization in Cloud Computing
16.1 Introduction16.2 Related Work16.3 Energy-Efficient Utilization of Resources in Cloud Computing Systems16.4 Complementarity Approach16.5 Simulation Results16.6 Discussion of Results16.7 ConclusionReferences
Chapter 17: Semantics-Based Resource Discovery in Large-Scale Grids
17.1 Introduction17.2 Related Work17.3 Virtual Organization Formation17.4 Semantics-Based Resource Discovery in Virtual Organizations17.5 Prototype Implementation and Evaluation17.6 ConclusionReferences
Chapter 18: Game-Based Models of Grid User's Decisions in Security-Aware Scheduling
18.1 Introduction18.2 Security-Aware Scheduling Problems in Computational Grids18.3 Game Models in Security-Aware Grid Scheduling18.4 Case Study: Approximating the Equilibrium States of the End Users' Symmetric Game Using the Genetic Metaheuristics18.5 ConclusionReferences
Chapter 19: Addressing Open Issues on Performance Evaluation in Cloud Computing
19.1 Introduction19.2 Benchmarking Approaches19.3 Monitoring in Cloud Computing19.4 Attack Countermeasures in Cloud Computing19.5 ConclusionReferences
Chapter 20: Broker-Mediated Cloud-Aggregation Mechanism Using Markovian Queues for Scheduling Bag-of-Tasks (BoT) Applications
20.1 Introduction20.2 Literature Review and Contributions20.3 Problem Setting and Notations20.4 Proposed Cloud Aggregation Mechanism20.5 Performance Evaluation and Discussions20.6 Discussions20.7 ConclusionReferences
Chapter 21: On the Design of a Budget-Conscious Adaptive Scheduler for Handling Large-Scale Many-Task Workflow Applications in Clouds
21.1 Introduction21.2 Related Work and Motivation21.3 System Model and Problem Setting21.4 Proposed Scheduling Algorithm21.5 Performance Evaluation and Results21.6 ConclusionReferences
Chapter 22: Virtualized Environment Issues in the Context of a Scientific Private Cloud
22.1 Introduction22.2 Related Works22.3 Methodology22.4 Experiments22.5 Conclusion22.6 GlossaryAcknowledgmentsReferences
Part 5: Other Topics Related to Network-Centric Computing and Its Applications
Chapter 23: In-Advance Bandwidth Scheduling in e-Science Networks
23.1 Introduction23.2 Temporal Network Model23.3 Single-Path Scheduling23.4 Multiple-Path Scheduling23.5 ConclusionAcknowledgmentReferences
Chapter 24: Routing and Wavelength Assignment in Optical Networks
24.1 Introduction24.2 Scheduling in Full-Wavelength Conversion Network24.3 Scheduling in Sparse Wavelength Conversion NetworkAcknowledgmentReferences
Chapter 25: Computational Graph Analytics for Massive Streaming Data
25.1 Introduction25.2 STINGER: A General-Purpose Data Structure for Dynamic Graphs25.3 Algorithm for Updating Clustering Coefficients25.4 Tracking Connected Components in Scale-Free Graphs25.5 Implementation25.6 Experimental Results25.7 Related Work25.8 ConclusionAcknowledgmentsReferences
Chapter 26: Knowledge Management for Fault-Tolerant Water Distribution
26.1 Introduction26.2 Related Work26.3 Agent-Based Model for WDN Operation26.4 Classes in WDN Ontology Framework26.5 Automated Failure Classification and Mitigation26.6 Validation of Automated Failure Mitigation26.7 ConclusionAcknowledgmentsReferences
Index

Content preview from Large Scale Network-Centric Distributed Systems

In-Advance Bandwidth Scheduling in e-Science Networks

Yan Li, Eunsung Jung, Sanjay Ranka, Nageswara S. Rao, and Sartaj Sahni

23.1 Introduction

23.2 Temporal Network Model

23.2.1 Slotted Time

23.2.2 Continuous Time

23.3 Single-Path Scheduling

23.3.1 Problem Definitions

23.3.2 Path Computation Algorithms

23.3.2.1 Fixed Slot

23.3.2.2 Maximum Bandwidth in a Slot

23.3.2.3 Maximum Duration

23.3.2.4 First Slot

23.3.2.5 All Slots

23.3.2.6 All Pairs, All Slots

23.3.3 Performance Metrics

23.3.3.1 Space Complexity

23.3.3.2 Time Complexity

23.3.3.3 Effectiveness

23.3.4 Experiments

23.4 Multiple-Path Scheduling

23.4.1 Problem Definition

23.4.1.1 Data Structures

23.4.2 Optimal Solution and N-Batch Heuristics

23.4.2.1 N-Batch Heuristics

23.4.3 Online Scheduling Algorithms

23.4.3.1 Greedy Algorithm

23.4.3.2 Greedy Scheduling with Finish Time Extension (GOS-E)

23.4.3.3 K-Path Algorithms

23.4.4 Experimental Evaluation

23.4.4.1 Experimental Framework

23.4.4.2 Single Start Time Scheduling (SSTS)

23.4.4.3 Multiple Start Time Scheduling (MSTS)

23.4.4.4 GOS vs. GOS-E

23.5 Conclusion

Acknowledgment

References

23.1 Introduction

Many large-scale scientific and commercial applications produce large amounts of data, of the order of terabytes to petabytes, which must be transferred across wide-area networks. For example, for an e-Science application, data sets produced on a supercomputer in Los Angeles may need to be streamed to a remote storage center in Houston for analysis. The results are ...

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Large Scale Network-Centric Distributed Systems

by Hamid Sarbazi-Azad, Albert Y. Zomaya

Contents

23.1 Introduction

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