National Changhua University of Education Institutional Repository : Item 987654321/11706

English | 正體中文 | 简体中文 | Items with full text/Total items : 6507/11669
Visitors : 29941917 Online Users : 481

RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Scope Adv. Search

Login ‧ Upload ‧ Help ‧ About ‧ Administer

NCUEIR > College of Engineering > Department and Graduate Institute of Computer Science and Information Engineering > Periodical Articles > Item 987654321/11706

Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/11706

Title:	Designing a Parallel Fuzzy Expert System Programming Model with Adaptive Load Balancing Capability for Cloud Computing
Authors:	Wu, Chao-Chin;Lai, Lien-Fu;Ke, Jenn-Yang;Jhan, Syun-Sheng;Chang, Yu-Shuo
Contributors:	資訊工程學系
Keywords:	Cloud computing;Parallel processing;Fuzzy expert system;Load balancing;Fuzzy CLIPS
Date:	2010-04
Issue Date:	2012-07-02T02:03:14Z
Publisher:	中華民國電腦學會
Abstract:	MapReduce is a programming model for processing and generating large data sets. It is used widely in cloud computing frequently. Programs written based on the MapReduce model are automatically parallelized and executed on a large cluster of commodity machines. Data partitioning, task scheduling and inter-process communication are all handled by the run-time system. Programmers have no need to learn the complicated techniques for parallel computation for efficient resource utilization in a large distributed system. In this paper, we introduce how to design a parallel fuzzy expert system programming model with adaptive load balancing capability based on the philosophy of MapReduce. In particular, we investigate how to utilize the feature of the fuzzy expert system language to design a dynamic scheduling scheme to improve the system performance. At runtime, the scheme adjusts the next chunk size for a worker by comparing the expected execution time and the real execution time of the current task assigned to the worker. Experimental results show the proposed scheduling scheme can improve the system performance significantly.
Relation:	Journal of Computers, 21(1): 38-48
Appears in Collections:	[Department and Graduate Institute of Computer Science and Information Engineering] Periodical Articles

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	621	View/Open

Loading...