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A Cost-Efficient and Reliable Resource Allocation Model Based on Cellular Automaton Entropy for Cloud Project Scheduling

Chen, Huankai, Wang, Frank Z., Helian, Na (2013) A Cost-Efficient and Reliable Resource Allocation Model Based on Cellular Automaton Entropy for Cloud Project Scheduling. International Journal of Advanced Computer Science and Applications, 4 (4). pp. 7-14. ISSN 2156-5570.

Abstract

Resource allocation optimization is a typical cloud project scheduling problem: a problem that limits a cloud system’s ability to execute and deliver a project as originally planned. The entropy, as a measure of the degree of disorder in a system, is an indicator of a system’s tendency to progress out of order and into a chaotic condition, and it can thus serve to measure a cloud system’s reliability for project scheduling. In this paper, cellular automaton is used for modeling the complex cloud project scheduling system. Additionally, a method is presented to analysis the reliability of cloud scheduling system by measuring the average resource entropy (ARE). Furthermore, a new cost-efficient and reliable resource allocation (CERRA) model is proposed based on cellular automaton entropy to aid decision maker for planning projects on the cloud. At last, the proposed model is designed using Matlab toolbox and simulated with three basic cloud scheduling algorithm, First Come First Served Algorithm (FCFS), Min-Min Algorithm and Max-Min Algorithm. The simulation results show that the proposed model can lead to achieve a cost-efficient and reliable resource allocation strategy for running projects on the cloud environment.

Item Type: Article
Uncontrolled keywords: Resource Allocation; Cloud Project Scheduling; Entropy; Cellular Automaton; Cost-efficiency; Reliability; Complex System; Local Activity; Global Order; Disorder
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Data Science
Depositing User: H. Chen
Date Deposited: 03 Jun 2013 12:11 UTC
Last Modified: 29 May 2019 10:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/34031 (The current URI for this page, for reference purposes)
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