Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
Risk-Resilient Heuristics and Genetic Algorithms for Security-Assured Grid Job Scheduling
IEEE Transactions on Computers
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Reliability and Scheduling on Systems Subject to Failures
ICPP '07 Proceedings of the 2007 International Conference on Parallel Processing
Push-Pull: Deterministic Search-Based DAG Scheduling for Heterogeneous Cluster Systems
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Bi-criteria Scheduling of Scientific Workflows for the Grid
CCGRID '08 Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid
Multi-objective planning for workflow execution on Grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Reliability-Driven Reputation Based Scheduling for Public-Resource Computing Using GA
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
Future Generation Computer Systems
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To optimize makespan and reliability for workflow applications, most existing works use list heuristics rather than genetic algorithms (GAs) which can usually give better solutions. In addition, most existing GAs evolve a scheduling solution randomly, which may give invalid solutions or lead to slow convergence of the algorithm. In this paper, we define three heuristics for GAs to decide the priorities for a resource and a task dynamically. We propose Look-Ahead Genetic Algorithm (LAGA) to optimize both makespan and reliability for workflow applications. It uses a novel evolution and evaluation mechanism: the genetic operators evolve the task-resource mapping for a scheduling solution, while the solution’s task order is determined in the evaluation step using our new max-min strategy, which is specifically proposed for GAs. Our experiments show that LAGA can provide better solutions than existing list heuristics and evolve to better solutions more quickly than a traditional genetic algorithm.