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Multiprocessor Task Graph Scheduling Using a Novel Graph-Like Learning Automata
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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.1 (2015.02)바로가기
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- 페이지
- pp.41-54
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A240287
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원문정보
초록
- 영어
- Optimized task scheduling is one of the most important challenges in multiprocessor environments such as parallel and distributed systems. In such these systems, each parallel program is decomposed into the smaller segments so-called tasks. Task execution times, precedence constrains and communication costs are modeled by using a directed acyclic graph (DAG) named task graph. The goal is to minimize the program finish-time (makespan) by means of mapping the tasks to the processor elements in such a way that precedence constrains are preserved. This problem is shown to be NP-hard in general form and some restricted ones. Therefore, utilization of heuristic and meta-heuristic approaches to solve this problem is logical. Learning automata (LA) is an abstract model to interact with stochastic environment, which tries to reform itself based on the environment feedback. Although a learning automaton itself is a simple component, a group of them by cooperating each other can show complicated behavior, and can coverage to desired solutions under appropriate learning algorithm. In this paper, an ingenious graph-like learning automata in which each task in the task graph is represented by a learning automaton tries to solve the multiprocessor task-scheduling problem in a collective manner. Set of different experiments on various real-world task-graphs has been done and archived results are so promising compared to the traditional methods and genetic algorithm.
목차
Abstract
1. Introduction
2. Multiprocessor Task Scheduling
2.1 The HLFET Algorithm
2.2 The MCP Algorithm
2.3 The DLS Algorithm
2.4 The ETF Algorithm
3. Learning Automata
4. The Proposed Approach
5. Implementation and Results
5.1 Reward Parameter of Learning Automata
5.2 Priority Measurement
5.3 Compare with Traditional Heuristics
5.4 Compare with Genetic Algorithm
6. Conclusion
References
키워드
Learning automata multiprocessor task scheduling parallel and distributed systems task graph.저자
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.1
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