Improved Particle Optimization Algorithm Solving Hadoop Task Scheduling Problem

DOI

10.2991/icicci-15.2015.3How to use a DOI?

Keywords

Keywords-Task scheduling; Estimation of Distribution; Particle Swarm Optimization; Cloud computing

Abstract

Abstract—Cloud computing to provide service for the user group is huge, so the number of cloud computer’s tasks is enormous, the system handle large tasks all the time so that task scheduling is the key and difficult points in the cloud. This article make research on how to make full use of cloud resources for task efficiently scheduling. This paper proposes an Improved Particle Swarm-Estimation of Distribution optimization Algorithm (IPS-EDA) based on task allocation strategy. The task scheduling strategy is optimization strategy based on improved particle swarm algorithm, which introduce estimation of distribution algorithm (EDA) based probabilistic model and random sampling theory, the proposed algorithm does not fall into local optimum. The simulation results show that the performance of IPS-EDA has been greatly improved provides better load balancing and resource utilization

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jun Xu
AU  - Yong Tang
PY  - 2015/09
DA  - 2015/09
TI  - Improved Particle Optimization Algorithm Solving Hadoop Task Scheduling Problem
BT  - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics
PB  - Atlantis Press
SP  - 11
EP  - 14
SN  - 1951-6851
UR  - https://doi.org/10.2991/icicci-15.2015.3
DO  - 10.2991/icicci-15.2015.3
ID  - Xu2015/09
ER  -