Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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A Multiobjective Genetic Algorithm for Static Scheduling of Real-time Tasks

다목적 유전 알고리즘을 이용한 실시간 태스크의 정적 스케줄링 기법

  • 오재원 (서울대학교 컴퓨터공학과) ;
  • 김희천 (한국방송통신대학교 컴퓨터과학) ;
  • 우치수 (서울대학교 컴퓨터공학부)
  • Published : 2004.03.01
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Abstract

We consider the problem of scheduling tasks of a precedence constrained task graph, where each task has its execution time and deadline, onto a set of identical processors in a way that simultaneously minimizes the number of processors required and the total tardiness of tasks. Most existing approaches tend to focus on the minimization of the total tardiness of tasks. In another methods, solutions to this problem are usually computed by combining the two objectives into a simple criterion to be optimized. In this paper, the minimization is carried out using a multiobjective genetic algorithm (GA) that independently considers both criteria by using a vector-valued cost function. We present various GA components that are well suited to the problem of task scheduling, such as a non-trivial encoding strategy. a domination-based selection operator, and a heuristic crossover operator We also provide three local improvement heuristics that facilitate the fast convergence of GA's. The experimental results showed that when compared to five methods used previously, such as list-scheduling algorithms and a specific genetic algorithm, the Performance of our algorithm was comparable or better for 178 out of 180 randomly generated task graphs.

본 본문에서는 다중 처리기 시스템에서 실시간 태스크를 정적으로 스케줄링하기 위한 새로운 기법을 제안한다. 태스크는 실행 시간과 마감시간을 지니고, 태스크 사이에는 선행 관계가 존재하며 이러한 사항을 태스크 그래프로 표현한다. 본 논문에서는 스케줄링을 위해 사용하는 처리기의 개수를 줄이면서 태스크들의 마감시간 지연의 총합을 최소화하는 스케줄을 생성하는 것에 목적을 둔다. 이 문제는 같은 단위로 측정할 수 없고 또한 서로 상충하는 두 가지 목적을 지닌 것이다. 그렇지만 기존 방법들은 마감시간 지연의 총합만을 최소화하려하거나 두 가지 목적을 하나의 기준으로 결합시킨 후 최적화하고자 한다. 본 논문에서는 두 개의 목적을 독립적으로 고려하며 최적화를 위하여 다목적 유전 알고리즘을 사용한다 태스크 스케줄링 문제에 적합한 문제 표현 전략, 우세 개념에 기초한 선택 연산, 그리고 교차 연산을 제시한다. 그리고 지역 개선 작업을 위해 세 개의 휴리스틱을 제안하였으며 이 것을 통해 유전 알고리즘의 수렴성을 높이고자 하였다. 성능 평가를 위해 기존에 알려진 유전 알고리즘과 4 개의 리스트 스케줄링 알고리즘과 비교한다. 평가 결과를 보면 제안한 기법이 180 개의 임의로 생성한 태스크 그래프 중에서 178 개에 대해 기존 5 개의 알고리즘과 유사하거나 더 나은 스케줄을 생성하였다.

Keywords

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