Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Efficient scheduling of arbitrary task graphs to multiprocessors using a parallel genetic algorithm
Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Resource-constrained project scheduling: a survey of recent developments
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Stork: Making Data Placement a First Class Citizen in the Grid
ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A framework for reliable and efficient data placement in distributed computing systems
Journal of Parallel and Distributed Computing - Special issue: Design and performance of networks for super-, cluster-, and grid-computing: Part I
Pegasus: A framework for mapping complex scientific workflows onto distributed systems
Scientific Programming
Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
The cost of doing science on the cloud: the Montage example
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Scientific workflow design for mere mortals
Future Generation Computer Systems
Data Staging Strategies and Their Impact on the Execution of Scientific Workflows
Proceedings of the second international workshop on Data-aware distributed computing
Exploiting replication and data reuse to efficiently schedule data-intensive applications on grids
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
JSSPP'05 Proceedings of the 11th international conference on Job Scheduling Strategies for Parallel Processing
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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Data-intensive workflows stage large amounts of data in and out of compute resources. The data staging strategies employed during the execution of such workflows can have a significant impact on the time taken to complete the execution or on the overall cost of the execution. We describe the problem of minimizing the overall time taken for execution and present a heuristic based on ordering clean-up jobs in the workflow. Next, we develop genetic algorithm based approaches to solving the same problem and demonstrate that the results obtained with the heuristic are comparable to the best results obtained with the genetic algorithm based approaches. We also describe the problem of minimizing the overall cost of execution and extend our genetic algorithm to generate schedules that vary the number of processors and the amount of storage provisioned for execution to generate low cost schedules.