Metaheuristics for scheduling in distributed computing environments
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Bibliographic Information
Metaheuristics for scheduling in distributed computing environments
（Studies in computational intelligence, v. 146）
Springer, c2008
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Library, Research Institute for Mathematical Sciences, Kyoto University数研
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Description and Table of Contents
Description
Grid computing has emerged as one of the most promising computing paradigms of the new millennium! Achieving high performance Grid computing requires techniques to efficiently and adaptively allocate jobs and applications to available resources in a large scale, highly heterogenous and dynamic environment.
This volume presents meta-heuristics approaches for Grid scheduling problems. Due to the complex nature of the problem, meta-heuristics are primary techniques for the design and implementation of efficient Grid schedulers. The volume brings new ideas, analysis, implementations and evaluation of meta-heuristic techniques for Grid scheduling, which make this volume novel in several aspects. The 14 chapters of this volume have identified several important formulations of the problem, which we believe will serve as a reference for the researchers in the Grid computing community.
Important features include the detailed overview of the various novel metaheuristic scheduling approaches, excellent coverage of timely, advanced scheduling topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and scheduling will find the comprehensive coverage of this book invaluable.
Table of Contents
Meta-heuristics for Grid Scheduling Problems.- Optimizing Routing and Backlogs for Job Flows in a Distributed Computing Environment.- Robust Allocation and Scheduling Heuristics for Dynamic, Distributed Real-Time Systems.- Supercomputer Scheduling with Combined Evolutionary Techniques.- Adapting Iterative-Improvement Heuristics for Scheduling File-Sharing Tasks on Heterogeneous Platforms.- Advanced Job Scheduler Based on Markov Availability Model and Resource Selection in Desktop Grid Computing Environment.- Workflow Scheduling Algorithms for Grid Computing.- Decentralized Grid Scheduling Using Genetic Algorithms.- Nature Inspired Meta-heuristics for Grid Scheduling: Single and Multi-objective Optimization Approaches.- Efficient Batch Job Scheduling in Grids Using Cellular Memetic Algorithms.- P2P B&B and GA for the Flow-Shop Scheduling Problem.- Peer-to-Peer Neighbor Selection Using Single and Multi-objective Population-Based Meta-heuristics.- An Adaptive Co-ordinate Based Scheduling Mechanism for Grid Resource Management with Resource Availabilities.
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