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Task Scheduling Using PSO Algorithm in Cloud Computing Environments
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- 발행기관
- 보안공학연구지원센터(IJGDC) 바로가기
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- 간행물
- International Journal of Grid and Distributed Computing 바로가기
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- 통권
- Vol.8 No.5 (2015.10)바로가기
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- 페이지
- pp.245-256
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A257203
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- The Cloud computing has become the fast spread in the field of computing, research and industry in the last few years. As part of the service offered, there are new possibilities to build applications and provide various services to the end user by virtualization through the internet. Task scheduling is the most significant matter in the cloud computing because the user has to pay for resource using on the basis of time, which acts to distribute the load evenly among the system resources by maximizing utilization and reducing task execution Time. Many heuristic algorithms have been existed to resolve the task scheduling problem such as a Particle Swarm Optimization algorithm (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Cuckoo search (CS) algorithms, etc. In this paper, a Dynamic Adaptive Particle Swarm Optimization algorithm (DAPSO) has been implemented to enhance the performance of the basic PSO algorithm to optimize the task runtime by minimizing the makespan of a particular task set, and in the same time, maximizing resource utilization. Also, .a task scheduling algorithm has been proposed to schedule the independent task over the Cloud Computing. The proposed algorithm is considered an amalgamation of the Dynamic PSO (DAPSO) algorithm and the Cuckoo search (CS) algorithm; called MDAPSO. According to the experimental results, it is found that MDAPSO and DAPSO algorithms outperform the original PSO algorithm. Also, a comparative study has been done to evaluate the performance of the proposed MDAPSO with respect to the original PSO.
목차
Abstract
1. Introduction
2. Related Work
3. The Scheduling System
4. The Basic Particle Swarm Optimization (Pso) Algorithm
5. Dynamic Adaptive Particle Swarm Optimization
6. Cuckoo Search Algorithm
7. The Proposed Mdapsotask Scheduling Algorithm
8. The Performance Evaluation
8.1. Experimental Settings
8.2. Performance Results
9. Conclusion
References
키워드
Cloud computing Particle Swarm Optimization Task scheduling저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of Grid and Distributed Computing
- 간기
- 격월간
- pISSN
- 2005-4262
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.8 No.5
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