DSQGG: An optimized genetic-based algorithm for scheduling in distributed grid

In this paper, a novel scheduling algorithm has been presented that is more efficient and has more reliability comparing similar algorithms. DSQGG is a novel algorithm that by defining new parameters and metrics, has decreased the delay and also response time of job. Also it satisfies the satisfaction level of servers. Several simulation results show that by using this new algorithm, a better makespan in total task process and a better performance of distributed system are being achieved. This way, quality of service in different levels of distributed processors is being increased and finally acknowledge delay of processes is being decreased significantly.

[1]  Ajith Abraham,et al.  Job Scheduling on Computational Grids Using Fuzzy Particle Swarm Algorithm , 2005 .

[2]  Enrique Alba,et al.  Efficient Batch Job Scheduling in Grids using Cellular Memetic Algorithms , 2007, IPDPS.

[3]  Ajith Abraham,et al.  Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm , 2006, KES.

[4]  A. Abraham,et al.  Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm , 2010, Future Gener. Comput. Syst..

[5]  Jan Broeckhove,et al.  Economic Grid Resource Management for CPU Bound Applications with Hard Deadlines , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[6]  M. Prakash,et al.  Fault Tolerance-Genetic Algorithm for Grid Task Scheduling using Check Point , 2007, Sixth International Conference on Grid and Cooperative Computing (GCC 2007).

[7]  Ajith Abraham,et al.  Variable Neighborhood Particle Swarm Optimization for Multi-objective Flexible Job-Shop Scheduling Problems , 2006, SEAL.

[8]  Vincenzo Di Martino,et al.  Sub optimal scheduling in a grid using genetic algorithms , 2003, Parallel Comput..

[9]  Andrew J. Page,et al.  Dynamic task scheduling using genetic algorithms for heterogeneous distributed computing , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[10]  Wael Abdulal,et al.  An improved rank-based genetic algorithm with limited iterations for grid scheduling , 2009, 2009 IEEE Symposium on Industrial Electronics & Applications.

[11]  Wanneng Shu,et al.  Optimal Resource Allocation on Grid Computing Using a Quantum Chromosomes Genetic Algorithm , 2007, Second Workshop on Digital Media and its Application in Museum & Heritages (DMAMH 2007).