A Scheduling Approach Considering Local Tasks in the Computational Grid

Task scheduling under a grid environment is an important research area, on which much attention has been paid. However, either in the meta-task scheduling problems or DAG (Direct Acyclic Graph) scheduling problems, it is usually assumed that tasks are submitted to dedicated hosts and that these tasks are processed in FIFO (First In First Out) order. This is not practical in a grid, in which a host may be shared between grid users and its owner and local tasks, which belong to resource owners, may compete with grid tasks for the hosts. EBGSA (Estimation Based Grid Scheduling Approach) is proposed, which allows for the simultaneous processing of grid tasks and local tasks. In EBGSA we use history information about the execution of tasks to estimate the performance of non-dedicated hosts. Two heuristic scheduling algorithms, MCT (Minimum Completion Time) and Min-min are selected to perform the simulation experiment. Both experiments obtain a smaller make span, proving EBGSA feasible for grid task scheduling.

[1]  Nirwan Ansari,et al.  A Genetic Algorithm for Multiprocessor Scheduling , 1994, IEEE Trans. Parallel Distributed Syst..

[2]  Chi-Kwong Li,et al.  Scheduling tasks in DAG to heterogeneous processor system , 1998, Proceedings of the Sixth Euromicro Workshop on Parallel and Distributed Processing - PDP '98 -.

[3]  Salim Hariri,et al.  Task scheduling algorithms for heterogeneous processors , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

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

[5]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .

[6]  Zhen Liu.  Scheduling of random task graphs on parallel processors , 1995, MASCOTS '95. Proceedings of the Third International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems.

[7]  Oscar H. Ibarra,et al.  Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors , 1977, JACM.

[8]  Alfredo Goñi Sarriguren.  Thinking in java , 2009 .

[9]  Chi-Kwong Li,et al.  Heterogeneous Dominant Sequence Cluster (HDSC): a low complexity heterogeneous scheduling algorithm , 1997, 1997 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing, PACRIM. 10 Years Networking the Pacific Rim, 1987-1997.

[10]  Hyunseung Choo,et al.  Decisive path scheduling: a new list scheduling method , 1997, Proceedings of the 1997 International Conference on Parallel Processing (Cat. No.97TB100162).

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

[12]  Binxing Fang,et al.  Scheduling algorithms for a fork DAG in a NOWs , 2000, Proceedings Fourth International Conference/Exhibition on High Performance Computing in the Asia-Pacific Region.

[13]  Yu-Kwong Kwok,et al.  Parallel program execution on a heterogeneous PC cluster using task duplication , 2000, Proceedings 9th Heterogeneous Computing Workshop (HCW 2000) (Cat. No.PR00556).

[14]  R. F. Freund,et al.  Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems , 1999, Proceedings. Eighth Heterogeneous Computing Workshop (HCW'99).

[15]  Martin Grajcar.  Genetic list scheduling algorithm for scheduling and allocation on a loosely coupled heterogeneous multiprocessor system , 1999, DAC '99.

[16]  Sung-Bong Yang,et al.  Task scheduling in distributed computing systems with a genetic algorithm , 1997, Proceedings High Performance Computing on the Information Superhighway. HPC Asia '97.