The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Scheduling in a Grid Computing Environment Using Genetic Algorithms
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
An Optimal Scheduling Algorithm Based on Task Duplication
ICPADS '01 Proceedings of the Eighth International Conference on Parallel and Distributed Systems
Sub optimal scheduling in a grid using genetic algorithms
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
The Anatomy of the Grid: Enabling Scalable Virtual Organizations
International Journal of High Performance Computing Applications
A Scheduling Model with Multi-Objective Optimization for Computational Grids using NSGA-II
International Journal of Applied Evolutionary Computation
A Computational Grid Scheduling Model To Maximize Reliability Using Modified GA
International Journal of Grid and High Performance Computing
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Grid computing is a new computing-framework to meet the growing computational demands. Computational grids provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources. However, task Scheduling is one of the key elements in the grid computing environment, and an efficient algorithm can help reduce the communication time between tasks. So far, the task scheduling algorithms in the grid computing environment have not been based on task duplication. However, the scheduling algorithms based on task duplication will generate too many task replications, which will enlarge the system loads and even add the makespan. As optimal scheduling of tasks is a strong NP-hard problem, this paper presents a scheduling algorithm based on genetic algorithm and task duplication, whose primary aim is to get the shortest makespan, and secondary aim to utilize less number of resources and duplicate less number of tasks. The chromosome coding method and the operator of genetic algorithm are discussed in detail. The relationship between subtasks can be obtained through the DAG. And the subtasks are ranked according to their depth-value, which can avoid the emergence of deadlock. The algorithm was compared with other scheduling algorithm based on GAs in terms of makespan, resource number and task replication number. The experimental results show the effectiveness of the proposed algorithm to the scheduling problem.