Specifications

Source title: ICIC Express Letters
Abbreviated source title: ICIC Express Lett.
Volume: 7
Issue: 10
Issue date: 2013
Publication year: 2013
Pages: 2707-2713
Language: English
ISSN: 1881803X
Document type: Journal article (JA)
Publisher: ICIC Express Letters Office, Tokai University, Kumamoto Campus, 9-1-1, Toroku,
Kumamoto, 862-8652, Japan
Abstract: With the rapid development of digitalized urban rail transit, the historical data
increase rapidly in urban rail transit line network, which leads to more and more large-size tasks
of data processing. Thus, it is necessary to process large-size data tasks in a cloud environment
due to insufficient capability of local data center. In order to minimize the scheduling length and
reduce resource rent cost, a large-size data processing task scheduling of urban rail transit line
network is modeled in the cloud, and a new scheduling algorithm based on particle swarm
optimization is proposed in this paper. In the algorithm, a fitness function is designed by using
scheduling length and resource rent cost. We also endow the particle position, velocity and
operations with reality meaning to fit the characteristic of line network. Experimental results
show that the proposed algorithm can not only decrease the scheduling length, but also reduce
the resource rent cost. © 2013 ICIC International.
Number of references: 9
Main heading: Data processing
Controlled terms: Algorithms - Cost reduction - Light rail transit - Particle swarm
optimization (PSO) - Scheduling
Uncontrolled terms: Cloud environments - Fitness functions - Historical data -
Large-size data tasks - Line network - Particle position - Task-scheduling - Urban
rail transit
Classification code: 682 Railroad Rolling Stock - 723 Computer Software, Data Handling and
Applications - 723.2 Data Processing and Image Processing - 912.2 Management - 921
Mathematics
Database: Compendex
Compilation and indexing terms, © 2013 Elsevier Inc.
5.
Accession number: 20134016808340
Title: A resource scheduling strategy in cloud computing based on multi-agent genetic
algorithm
Authors: Jiang, Wuxue1, 2 ; Zhang, Jing1 ; Li, Junhuai1 ; Hu, Hui3/;张璟;李军怀;;
Author affiliation: 1 School of Computer Science and Engineering, Xi'an University of
Technology, Xi'an 710048, Shaanxi, China
2 Department of Computer Engineering, Dongguan Polytechnic, Dongguan 523808, Guangdong,