ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud
ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud
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DOI：
10.1109/ipdps.2012.25
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发表时间：
2012-05
期刊：
影响因子：
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通讯作者：
Orna Agmon Ben-Yehuda;A. Schuster;A. Sharov;M. Silberstein;A. Iosup
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作者：
Orna Agmon Ben-Yehuda;A. Schuster;A. Sharov;M. Silberstein;A. Iosup
许多科学家通过在网格和云等计算环境的混合环境中执行大量类似的任务(机器人)来执行广泛的计算。尽管可靠性和成本在这些环境中可能会有很大差异，但没有工具可以帮助科学家选择既能完成最后期限又能满足预算的环境。针对这种情况，我们引入了专家机器人调度框架。我们的框架从一个大的搜索空间中系统地选择帕累托有效的调度策略，即在制造跨度和成本方面都能提供最好结果的策略。专家根据一个通用的、用户指定的效用函数从它们中选择最佳策略。通过仿真和在实际生产环境中的实验，我们证明了专家调度策略与普通调度策略相比，可以显著地减少制造周期和成本。对于在真实的网格+云混合环境中执行的生物信息机器人，我们展示了专家选择的调度策略与常用的调度策略相比，如何同时减少制造周期和成本30%-70%。
Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the Expert BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both make span and cost. Expert chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that Expert can substantially reduce both make span and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid + cloud environment, we show how the scheduling strategy selected by Expert reduces both make span and cost by 30%-70%, in comparison to commonly-used scheduling strategies.