Metaheuristics for Scheduling in Distributed Computing Environments

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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Markov;Peer-to-Peer computing;Workflow applications;algorithm;algorithms;computational intelligence;computer-aided design (CAD);evolution;genetic algorithms;heuristics;intelligence;metaheuristic;model;multi-objective optimization;optimization