最小方差分布式截止期调度
Minimal-Variance Distributed Deadline Scheduling
摘要：
许多现代的服务器可以动态地调整其服务容量以匹配传入的工作负载。然而，与此同时，服务能力的不可预测性和不稳定性往往造成业务和基础设施费用。在本文中，我们寻求最佳分布式算法的特点，最大限度地提高可预测性，稳定性，或两者兼而有之时，调度作业的最后期限。具体来说，我们表明，精确调度最大限度地减少了固定的平均值和方差的服务能力，严格的需求和截止日期的要求。对于更一般的设置，我们的特点是最小方差分布式政策的软需求的要求，软期限的要求，或两者兼而有之。最佳分布式政策的性能相比，最佳的集中式政策，通过推导出封闭形式的界限，并通过测试集中式和分布式算法，使用实际数据从加州理工学院的电动汽车充电设施和许多件的合成数据从不同的到达分布。此外，我们推导出的Pareto-最优性条件的分布式政策，平衡方差和均方的服务能力。最后，我们讨论了一个可扩展的部分集中式算法，使用集中的信息，以提高性能和一种方法来处理缺失的服务需求信息。
英文摘要：
Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, unpredictability and instability in service capacity often incur operational and infrastructure costs. In this paper, we seek to characterize optimal distributed algorithms that maximize the predictability, stability, or both when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes both the stationary mean and variance of the service capacity subject to strict demand and deadline requirements. For more general settings, we characterize the minimal-variance distributed policies with soft demand requirements, soft deadline requirements, or both. The performance of the optimal distributed policies is compared to that of the optimal centralized policy by deriving closed-form bounds and by testing centralized and distributed algorithms using real data from the Caltech electrical vehicle charging facility and many pieces of synthetic data from different arrival distribution. Moreover, we derive the Pareto-optimality condition for distributed policies that balance the variance and mean square of the service capacity. Finally, we discuss a scalable partially-centralized algorithm that uses centralized information to boost performance and a method to deal with missing information on service requirements.
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