The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Scheduling Multiprocessor Tasks with Genetic Algorithms
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Computers
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
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Condor-G: A Computation Management Agent for Multi-Institutional Grids
Cluster Computing
Performance Modeling and Prediction of Nondedicated Network Computing
IEEE Transactions on Computers
Implementation of Standard Genetic Algorithm on MIMD Machines
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
A Theoretical Investigation of a Parallel Genetic Algorithm
Proceedings of the 3rd International Conference on Genetic Algorithms
Adaptive Computing on the Grid Using AppLeS
IEEE Transactions on Parallel and Distributed Systems
Dynamic Load-Balancing via a Genetic Algorithm
ICTAI '01 Proceedings of the 13th IEEE International Conference on Tools with Artificial Intelligence
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Predicting application run times with historical information
Journal of Parallel and Distributed Computing
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
Enhancing genetic algorithms for dependent job scheduling in grid computing environments
The Journal of Supercomputing
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This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is two-fold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records and (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods, such as first-come-first-serve (FCFS) and random scheduling, heuristics, such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions.