The Triangular Pyramid Scheduling Model and algorithm for PDES in Grid

Abstract Grid is a perfect environment for the large scale Parallel Discrete Event Simulation (PDES), because its distribution and collaboration features match the PDES applications well. The PDES tasks or applications are modeled as a Directed Acyclic Graph (DAG), in which the simulation resources consist of three critical factors, simulation hosting machine (SHM), simulation service (SS) and simulation data (SD) in Grid environment. By solving the model we attempt to obtain an optimized triangular matching of the simulation resources on Grid, so that it can support the PDES activities better. We name the algorithm of solving the model Triangular Pyramid Scheduling (TPS). The PDES DAG is divided into three basic graph structures: Sequential structure, Fork structure, and Join structure. The TPS algorithm is developed based on these graph structures. The simulation results show that TPS algorithm can reduce the makespan and congestion, improve the simulation efficiency, and increase the resource utilization efficiency, compared to the existing algorithms.

[1]  Rajkumar Buyya,et al.  A Taxonomy of Workflow Management Systems for Grid Computing , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[2]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[3]  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..

[4]  Adam Arbree,et al.  Mapping Abstract Complex Workflows onto Grid Environments , 2003, Journal of Grid Computing.

[5]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[6]  F. M. Hughes,et al.  A pre-scheduled adaptive control scheme based upon system knowledge , 1995 .

[7]  Kalyan S. Perumalla.  Scaling time warp-based discrete event execution to 104 processors on a Blue Gene supercomputer , 2007, CF '07.

[8]  Richard M. Fujimoto,et al.  A scalable framework for parallel discrete event simulations on desktop grids , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[9]  Helen D. Karatza,et al.  Resource Allocation Strategies in a 2-Level Hierarchical Grid System , 2008, 41st Annual Simulation Symposium (anss-41 2008).

[10]  Richard M. Fujimoto,et al.  Parallel discrete event simulation , 1990, CACM.

[11]  Francine Berman,et al.  High-performance schedulers , 1998 .

[12]  T. Shepard,et al.  A Pre-Run-Time Scheduling Algorithm For Hard Real-Time Systems , 1991, IEEE Trans. Software Eng..

[13]  Ken Kennedy,et al.  TaskScheduling Strategies forWorkflow-based Applications inGrids , 2005 .

[14]  Jing-Chiou Liou,et al.  A comparison of general approaches to multiprocessor scheduling , 1997, Proceedings 11th International Parallel Processing Symposium.

[15]  Steven Tuecke,et al.  The Physiology of the Grid An Open Grid Services Architecture for Distributed Systems Integration , 2002 .

[16]  Selim G. Akl,et al.  Scheduling Algorithms for Grid Computing: State of the Art and Open Problems , 2006 .

[17]  Zhihui Du,et al.  A Pipeline Virtual Service Pre-Scheduling Pattern and its Application in Astronomy Data Processing , 2007, Simul..

[18]  Subhash Saini,et al.  GridFlow: workflow management for grid computing , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[19]  Radu Prodan,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005, SGMD.

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

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

[22]  M. Goul,et al.  Autonomic workflow execution in the grid , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[23]  Shensheng Zhang,et al.  A workflow model supporting pre-dispatching of tasks , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[24]  Helen D. Karatza,et al.  The impact of task service time variability on gang scheduling performance in a two-cluster system , 2009, Simul. Model. Pract. Theory.