National Changhua University of Education Institutional Repository : Item 987654321/11706
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 6491/11663
造访人次 : 24630480      在线人数 : 78
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻


题名: Designing a Parallel Fuzzy Expert System Programming Model with Adaptive Load Balancing Capability for Cloud Computing
作者: Wu, Chao-Chin;Lai, Lien-Fu;Ke, Jenn-Yang;Jhan, Syun-Sheng;Chang, Yu-Shuo
贡献者: 資訊工程學系
关键词: Cloud computing;Parallel processing;Fuzzy expert system;Load balancing;Fuzzy CLIPS
日期: 2010-04
上传时间: 2012-07-02T02:03:14Z
出版者: 中華民國電腦學會
摘要: 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.
關聯: Journal of Computers, 21(1): 38-48
显示于类别:[資訊工程學系] 期刊論文


档案 大小格式浏览次数



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