A Grid Workload Modeling Approach for Intelligent Grid

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As more and more grid applications are put into use, the performance information plays a more important role in evaluating the running status of the grid system. However, most existing monitoring tools only provide system-level performance data, which are often massive in storage and meanwhile contain either duplicate or missing, even erroneous raw data. In order to make the collected data more easily to understand and thus reusable, in this paper we discuss grid workload modeling approaches to better serve the grid environment. We extract three key workload objects from numerous monitoring entities as the backbone of the workload and put them into use to examine the completeness of a performance trace. We present a methodology for accessing and interpreting grid workload information. A grid workload, which is recorded as a structured collection of monitoring information that is kept in repository continuously updated as a reflection of the execution, is studied at three hierarchical levels. From a goal-oriented perspective, this systematic approach could help use the workload data to guide and improve the resource mappings and fine tune the application and system, opening the way towards an intelligent grid environment.
Original languageUnknown
Title of host publicationIEEE International Conference on Networking, Sensing and Control
PublisherIEEE
Pages811-816
Volume1
ISBN (Print)978-1-4244-3491-6
DOIs
Publication statusPublished - 1 Jan 2009
EventIEEE International Conference on Networking, Sensing and Control (ICNSC 2009) -
Duration: 1 Jan 2009 → …

Conference

ConferenceIEEE International Conference on Networking, Sensing and Control (ICNSC 2009)
Period1/01/09 → …

Cite this

Duarte, V. M. A., & Cunha, J. A. C. E. (2009). A Grid Workload Modeling Approach for Intelligent Grid. In IEEE International Conference on Networking, Sensing and Control (Vol. 1, pp. 811-816). IEEE. https://doi.org/10.1109/ICNSC.2009.4919384