通过Reddit实现协同：自适应复制策略和基本限制
Synergy via Redundancy: Adaptive Replication Strategies and Fundamental Limits
摘要：
多服务器系统的最大可能吞吐量（或作业完成速率）通常是各个服务器的服务速率之和。最近的工作表明，启动多个副本的作业，并取消他们尽快完成一个副本可以提高吞吐量，特别是当服务时间分布有很大的变化。这意味着冗余实际上可以在服务器之间创建协同效应，使得它们的总体吞吐量大于单个服务器的总和。这项工作的目的是找到的基本限制的吞吐量的提升所取得的作业复制和最佳的复制政策，以实现it.While大多数以前的作品考虑前期复制政策，我们扩大了一套可能的政策，延迟推出的副本。最佳自适应复制策略的搜索可以制定为一个马尔可夫决策过程，使用它，我们提出了两个近视复制策略，MaxRate和AdaRep，自适应复制作业。为了量化这些和其他政策的最优性差距，我们推导出的服务能力的上限，这提供了基本的限制吞吐量的冗余系统。
英文摘要：
The maximum possible throughput (or the rate of job completion) of a multi-server system is typically the sum of the service rates of individual servers. Recent work shows that launching multiple replicas of a job and canceling them as soon as one copy finishes can boost the throughput, especially when the service time distribution has high variability. This means that redundancy can, in fact, create synergy among servers such that their overall throughput is greater than the sum of individual servers. This work seeks to find the fundamental limit of the throughput boost achieved by job replication and the optimal replication policy to achieve it. While most previous works consider upfront replication policies, we expand the set of possible policies to delayed launch of replicas. The search for the optimal adaptive replication policy can be formulated as a Markov Decision Process, using which we propose two myopic replication policies, MaxRate and AdaRep, to adaptively replicate jobs. In order to quantify the optimality gap of these and other policies, we derive upper bounds on the service capacity, which provide fundamental limits on the throughput of queueing systems with redundancy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
