An approach to grid scheduling optimization based on fuzzy association rule mining

This paper presents a grid scheduling optimization technique based on knowledge discovery. The main idea is to transform the grid monitoring data into a performance data set, extract the association patterns of performance data through fuzzy association rule mining, then construct optimization logic according to the mining results, and finally optimize the grid scheduling. In the process of data mining, a method of association rule mining is proposed based on time-window and fuzzy set concepts, which can mine data for quantitative attribute value based on the attribute and time dimensions in grid performance data set

[1]  Graham R. Nudd,et al.  Run-Time Optimization Using Dynamic Performance Prediction , 2000, HPCN Europe.

[2]  Stephen A. Jarvis,et al.  An investigation into the application of different performance prediction techniques to e-Commerce applications , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[3]  Tzung-Pei Hong,et al.  Mining fuzzy sequential patterns from quantitative data , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[4]  Simon Fraser.  MULTI-DIMENSIONAL SEQUENTIAL PATTERN MINING , 2001 .

[5]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[6]  T. Hong,et al.  Mining fuzzy sequential patterns from multiple-item transactions , 2001, Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569).

[7]  Carla E. Brodley,et al.  Predictive application-performance modeling in a computational grid environment , 1999, Proceedings. The Eighth International Symposium on High Performance Distributed Computing (Cat. No.99TH8469).

[8]  Ming Wu,et al.  Grid Harvest Service: a system for long-term, application-level task scheduling , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[9]  Salim Hariri,et al.  Self-adapting, self-optimizing runtime management of Grid applications using PRAGMA , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[10]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.