A Genetic Algorithm to Increase the Throughput of the Computational Grids 1

High throughput computing (HTC) is of great importance in grid computing environments. HTC is aimed at minimizing the total makespan of all of the tasks submitted to the grid environment in long execution of the system. To achieve HTC in grids, suitable task scheduling algorithms should be applied to dispatch the submitted tasks to the computational resources appropriately. In this paper, a new task scheduling algorithm is proposed to assign the tasks to the grid resources with goal of minimizing the total makespan of the environment. The proposed algorithm uses genetic approach to find the most suitable match between the tasks and grid resources. The simulation results obtained from applying the proposed algorithm to schedule independent and sequential tasks to the grid resources demonstrate the applicability of the algorithm in grid environments.

[1]  Yong Xue,et al.  High Throughput Computing for Spatial Information Processing (HIT-SIP) System on Grid Platform , 2005, EGC.

[2]  Maozhen Li,et al.  The grid - core technologies , 2005 .

[3]  Scott M. Thede.  An introduction to genetic algorithms , 2004 .

[4]  Gregory Levitin,et al.  Optimal service task partition and distribution in grid system with star topology , 2008, Reliab. Eng. Syst. Saf..

[5]  Ehsan Ullah Munir,et al.  QoS Sufferage Heuristic for Independent Task Scheduling in Grid , 2007 .

[6]  Eduardo Huedo,et al.  Benchmarking of high throughput computing applications on Grids , 2006, Parallel Comput..

[7]  Anthony A. Maciejewski,et al.  Study of an Iterative Technique to Minimize Completion Times of Non-Makespan Machines , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[8]  Saeed Parsa,et al.  Modeling and Throughput Analysis of Grid Task Scheduling Using Stochastic Petri Nets , 2009, PDPTA.

[9]  Reza Entezari-Maleki,et al.  Task scheduling modelling and reliability evaluation of grid services using coloured Petri nets , 2010, Future Gener. Comput. Syst..

[10]  SiegelHoward Jay,et al.  Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach , 1997 .

[11]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[12]  Morteza Analoui,et al.  Resource Scheduling in Desktop Grid by Grid-JQA , 2008, 2008 The 3rd International Conference on Grid and Pervasive Computing - Workshops.

[13]  Kobra Etminani,et al.  A Min-Min Max-Min Selective Algorithm for Grid Task Scheduling , 2007, 2007 3rd IEEE/IFIP International Conference in Central Asia on Internet.

[14]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[15]  Gregor von Laszewski,et al.  QoS guided Min-Min heuristic for grid task scheduling , 2003, Journal of Computer Science and Technology.

[16]  R. F. Freund,et al.  Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems , 1999, J. Parallel Distributed Comput..

[17]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[18]  Ching-Hsien Hsu,et al.  Performance effective pre-scheduling strategy for heterogeneous grid systems in the master slave paradigm , 2007, Future Gener. Comput. Syst..

[19]  Morteza Analoui,et al.  Grid_JQA: A QoS Guided Scheduling Algorithm for Grid Computing , 2007, Sixth International Symposium on Parallel and Distributed Computing (ISPDC'07).

[20]  Saeed Parsa,et al.  RASA-A New Grid Task Scheduling Algorithm , 2009, J. Digit. Content Technol. its Appl..

[21]  Shu-Chin Wang,et al.  A minimized makespan scheduler with multiple factors for Grid computing systems , 2009, Expert Syst. Appl..

[22]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..