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학술지 Multihybrid Job Scheduling for Fault-Tolerant Distributed Computing in Policy-Constrained Resource Networks
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저자
문용혁, 윤찬현
발행일
201505
출처
Computer Networks : The International Journal of Telecommunications Networking, v.82, pp.81-95
ISSN
1389-1286
출판사
Elsevier
DOI
https://dx.doi.org/10.1016/j.comnet.2015.02.030
협약과제
14MS2200, MTM기반 단말 및 차세대 무선랜 보안 기술 개발, 조현숙
초록
Unpredictable fluctuations in resource availability often lead to rescheduling decisions that sacrifice a success rate of job completion in batch job scheduling. To overcome this limitation, we consider the problem of assigning a set of sequential batch jobs with demands to a set of resources with constraints such as heterogeneous rescheduling policies and capabilities. The ultimate goal is to find an optimal allocation such that performance benefits in terms of makespan and utilization are maximized according to the principle of Pareto optimality, while maintaining the job failure rate close to an acceptably low bound. To this end, we formulate a multihybrid policy decision problem (MPDP) on the primary-backup fault tolerance model and theoretically show its NP-completeness. The main contribution is to prove that our multihybrid job scheduling (MJS) scheme confidently guarantees the fault-tolerant performance by adaptively combining jobs and resources with different rescheduling policies in MPDP. Furthermore, we demonstrate that the proposed MJS scheme outperforms the five rescheduling heuristics in solution quality, searching adaptability and time efficiency by conducting a set of extensive simulations under various scheduling conditions.
키워드
Distributed computing, Fault tolerance, Genetic algorithm, Job scheduling, Multiobjective optimization, Policy heterogeneity
KSP 제안 키워드
Batch job, Decision problem, Failure Rate, Fault tolerance, Fault-tolerant, Genetic Algorithm, Job failure, Multi-objective optimization(MOP), NP-completeness, Optimal Allocation, Pareto Optimality