Typical schedulers in multi-tenancy environments make use of reactive, feedback-oriented mechanisms based on performance counters to avoid resource contention but suffer from detection lag and loss of performance. In this paper, we address these limitations by exploring the utility of predictive analysis through dynamic forecasting of applications' resource-heavy regions during its execution. Our compiler framework classifies loops in programs and leverages traditional compiler analysis along with learning mechanisms to quantify their behaviour. Based on the predictability of their execution time, it then inserts different types of beacons at their entry/exit points. The information produced by beacons in multiple processes is aggregated and analyzed by the proactive scheduler to respond to the anticipated workload requirements. For throughput environments, we develop a framework that demonstrates high-quality predictions and improvements in throughput over CFS by 76.78% on an average and up to 3.2x on Amazon Graviton2 Machine on consolidated workloads across 45 benchmarks.

翻译：多租赁环境中的典型调度员利用基于业绩对数的被动、反馈导向机制,避免资源争议,但因探测滞后和业绩损失而受到影响。本文通过探讨预测分析的效用,对应用程序在执行期间资源过多的区域进行动态预测,以解决这些局限性。我们的汇编员框架将程序循环分类,并利用传统的汇编员分析机制来量化其行为。根据其执行时间的可预测性,然后在其出入境点插入不同类型的信标。多个程序中的信标产生的信息由积极主动的调度员汇总和分析,以应对预期的工作量要求。关于吞吐环境,我们开发了一个框架,以显示高质量的预测以及超过CFS的吞吐量平均为76.78%,在亚马逊 Graviton2机器上平均为3.2%,在45个基准的综合工作量方面,对亚马逊 Graviton2机器进行整合。