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Cooperative Scheduling of Bag-of-Tasks Workflows on Hybrid Clouds

机译:混合云上的任务袋工作流协同调度

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We address the problem of scheduling a class of large-scale applications inspired from real-world on hybrid Clouds, characterized by a large number of homogeneous and concurrent tasks that are the main sources of bottlenecks but open great potential for optimization. We formulate the scheduling problem as a new sequential cooperative game and propose a communication- and storage-aware multi-objective algorithm that optimizes two user objectives (execution time and economic cost) while fulfilling two constraints (network bandwidth and storage requirements). We present comprehensive experiments using both simulation and real-world applications that demonstrate the efficiency and effectiveness of our approach in terms of algorithm complexity, make span, cost, system-level efficiency, fairness, and other aspects compared with other related algorithms.
机译:我们解决了在混合云上调度一类受现实世界启发的大型应用程序的问题,该应用程序具有大量同类任务和并发任务,这些任务是瓶颈的主要根源,但具有极大的优化潜力。我们将调度问题表述为一种新的顺序合作博弈,并提出一种可感知通信和存储的多目标算法,该算法可在满足两个约束(网络带宽和存储要求)的同时优化两个用户目标(执行时间和经济成本)。我们提供了使用仿真和实际应用程序进行的全面实验,这些实验与其他相关算法相比,从算法复杂性,制造跨度,成本,系统级效率,公平性和其他方面证明了我们方法的效率和有效性。

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