半批多服务器作业模型中的帕累托最优调度

中文摘要 AI 辅助

在大规模计算系统中，作业通常需要异构服务器分配。大作业占用大部分服务器，对延迟敏感；小作业填充剩余容量以维持吞吐量。为此引入半批多服务器作业（MSJ）框架，大作业按泊松过程到达且需所有服务器，小作业只需一台服务器且随时可用。证明在半批MSJ模型中，大作业平均响应时间和小作业吞吐量的帕累托前沿有简单精确的特征。由一系列护航策略生成，系统先服务小作业直到k个大作业到达，然后切换服务大作业，还有相邻护航策略的凸组合。结果完全通用且非渐近，适用于每个稳定到达率λ、服务器数量n和大作业大小分布S。

英文摘要

In large-scale computing systems, jobs often demand heterogeneous server allocations: large jobs that occupy a substantial fraction of the servers are of high importance and are thus latency-sensitive, while small jobs fill in the remaining capacity to maintain throughput. To model this dynamic, we introduce the half-batch multiserver-job (MSJ) framework, a queueing model in which large jobs arrive according to a Poisson process and require all servers simultaneously, while small jobs, each needing only one server, are always available. We prove that, in the half-batch MSJ model, the Pareto frontier for large-job mean response time and small-job throughput admits a simple and exact characterization. It is generated by a family of convoy policies, under which the system serves small jobs until $k$ large jobs have arrived and then switches to serving large jobs, together with convex combinations of neighboring convoy policies. Our result is fully general and non-asymptotic, holding for every stable arrival rate $λ$, every number of servers $n$, and every large-job size distribution $S$.

发表机构

• Northwestern University（西北大学）

机构由 AI 辅助整理，请以论文原文为准。