Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
The grid
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
Journal of Parallel and Distributed Computing
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Predicting Application Run Times Using Historical Information
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
A Unified Resource Scheduling Framework for Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Dynamic Task Scheduling using Genetic Algorithms for Heterogeneous Distributed Computing
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Pace--A Toolset for the Performance Prediction of Parallel and Distributed Systems
International Journal of High Performance Computing Applications
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
The grid core technologies
Performance prediction and its use in parallel and distributed computing systems
Future Generation Computer Systems - Systems performance analysis and evaluation
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
MapReduce: simplified data processing on large clusters
Communications of the ACM - 50th anniversary issue: 1958 - 2008
Scientific Programming - Scientific Workflows
WARPP: a toolkit for simulating high-performance parallel scientific codes
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
Evaluating Heuristics for Grid Workflow Scheduling
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Adaptive grid job scheduling with genetic algorithms
Future Generation Computer Systems
PGGA: A predictable and grouped genetic algorithm for job scheduling
Future Generation Computer Systems - Parallel input/output management techniques (PIOMT) in cluster and grid computing
A MapReduce-based distributed SVM algorithm for automatic image annotation
Computers & Mathematics with Applications
Enhancing genetic algorithms for dependent job scheduling in grid computing environments
The Journal of Supercomputing
Enhancing genetic algorithms for dependent job scheduling in grid computing environments
The Journal of Supercomputing
The Journal of Supercomputing
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Genetic Algorithms (GAs) are stochastic search techniques based on principles of natural selection and recombination that attempt to find optimal solutions in polynomial time by manipulating a population of candidate solutions. GAs have been widely used for job scheduling optimisation in both homogeneous and heterogeneous computing environments. When compared with list scheduling heuristics, GAs can potentially provide better solutions but require much longer processing time and significant experimentation to determine GA parameters. This paper presents a GA for scheduling dependent jobs in grid computing environments. A number of selection and pre-selection criteria for the GA are evaluated with an aim to improve GA performance in job scheduling optimization. A Task Matching with Data scheme is proposed as a GA mutation operator. Furthermore, the effect of the choice of heuristics for seeding the GA is investigated.