In this paper we build a case for providing job completion time predictions to cloud users, similar to the delivery date of a package or arrival time of a booked ride. Our analysis reveals that providing predictability can come at the expense of performance and fairness. Existing cloud scheduling systems optimize for extreme points in the trade-off space, making them either extremely unpredictable or impractical. To address this challenge, we present PCS, a new scheduling framework that aims to provide predictability while balancing other traditional objectives. The key idea behind PCS is to use Weighted-Fair-Queueing (WFQ) and find a suitable configuration of different WFQ parameters (e.g., class weights) that meets specific goals for predictability. It uses a simulation-aided search strategy, to efficiently discover WFQ configurations that lie on the Pareto front of the trade-off space between these objectives. We implement and evaluate PCS in the context of DNN job scheduling on GPUs. Our evaluation, on a small scale GPU testbed and larger-scale simulations, shows that PCS can provide accurate completion time estimates while marginally compromising on performance and fairness.

翻译：本文论证了向云用户提供作业完成时间预测的可行性，类似于包裹递送日期或预约车辆到达时间。分析表明，提供可预测性可能会以牺牲性能和公平性为代价。现有云调度系统在权衡空间中的极端点进行优化，导致其要么极度不可预测，要么不切实际。为应对这一挑战，我们提出PCS——一种旨在平衡可预测性与传统目标的新型调度框架。PCS的核心思想是采用加权公平队列（WFQ）并通过寻找不同WFQ参数（如类别权重）的合适配置来满足特定的可预测性目标。该框架采用仿真辅助搜索策略，高效发现位于多目标权衡空间帕累托前沿上的WFQ配置。我们在GPU上的DNN作业调度场景中实现了PCS并进行了评估。基于小规模GPU测试平台与大规模仿真的实验结果表明，PCS能在轻微牺牲性能与公平性的前提下，提供准确的作业完成时间估计。