English  |  正體中文  |  简体中文  |  Items with full text/Total items : 6487/11649
Visitors : 28659597      Online Users : 411
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search

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:[資訊工程學系] 期刊論文

Files in This Item:

File SizeFormat

All items in NCUEIR are protected by copyright, with all rights reserved.


DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback