On-Line Task Granularity Adaptation for Dynamic Grid Applications

Deploying lightweight tasks on grid resources would let the communication overhead dominate the overall application processing time Our aim is to increase the resulting computation-communication ratio by adjusting the task granularity at the grid scheduler We propose an on-line scheduling algorithm which performs task grouping to support an unlimited number of user tasks, arriving at the scheduler at runtime The algorithm decides the task granularity based on the dynamic nature of a grid environment: task processing requirements; resource-network utilisation constraints; and users QoS requirements Simulation results reveal that our algorithm reduces the overall application processing time and communication overhead significantly while satisfying the runtime constraints set by the users and the resources.

[1]  Michael Hurwicz.  Behind the benchmarks , 1998 .

[2]  P. D. Coddington,et al.  Scheduling Independent Tasks on Metacomputing Systems , 1999 .

[3]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[4]  Rajkumar Buyya,et al.  Grids and Grid technologies for wide‐area distributed computing , 2002, Softw. Pract. Exp..

[5]  Francine Berman,et al.  Overview of the Book: Grid Computing – Making the Global Infrastructure a Reality , 2003 .

[6]  Ibm Redbooks.  Introduction to Grid Computing With Globus , 2003 .

[7]  GWD-C.  A Hierarchy of Network Performance Characteristics for Grid Applications and Services , 2003 .

[8]  Rajkumar Buyya,et al.  A Dynamic Job Grouping-Based Scheduling for Deploying Applications with Fine-Grained Tasks on Global Grids , 2005, ACSW.

[9]  David Abramson,et al.  Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids , 2005, Concurr. Comput. Pract. Exp..

[10]  Rajkumar Buyya,et al.  Neuroscience instrumentation and distributed analysis of brain activity data: a case for eScience on global Grids: Research Articles , 2005 .

[11]  Boleslaw K. Szymanski,et al.  The Internet Operating System: Middleware for Adaptive Distributed Computing , 2006, Int. J. High Perform. Comput. Appl..

[12]  Ng Wai Keat,et al.  SCHEDULING FRAMEWORK FOR BANDWIDTH-AWARE JOB GROUPING-BASED SCHEDULING IN GRID COMPUTING , 2006 .

[13]  Jun Feng,et al.  Resource usage policy expression and enforcement in grid computing , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[14]  Eugenio Zimeo,et al.  A Framework for QoS-based Resource Brokering in Grid Computing , 2007, WEWST.

[15]  Angela C. Sodan,et al.  Group-Based Optimizaton for Parallel Job Scheduling with Scojo-PECT-O , 2008, 2008 22nd International Symposium on High Performance Computing Systems and Applications.

[16]  C. Eswaran,et al.  An Adaptive And Parameterized Job Grouping Algorithm For Scheduling Grid Jobs , 2008, 2008 10th International Conference on Advanced Communication Technology.