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In this paper we build a case for providing job completion time predictionsto cloud users, similar to the delivery date of a package or arrival time of abooked ride. Our analysis reveals that providing predictability can come at theexpense of performance and fairness. Existing cloud scheduling systems optimizefor extreme points in the trade-off space, making them either extremelyunpredictable or impractical.  To address this challenge, we present PCS, a new scheduling framework thataims to provide predictability while balancing other traditional objectives.The key idea behind PCS is to use Weighted-Fair-Queueing (WFQ) and find asuitable configuration of different WFQ parameters (e.g., class weights) thatmeets specific goals for predictability. It uses a simulation-aided searchstrategy, to efficiently discover WFQ configurations that lie on the Paretofront of the trade-off space between these objectives. We implement andevaluate PCS in the context of DNN job scheduling on GPUs. Our evaluation, on asmall scale GPU testbed and larger-scale simulations, shows that PCS canprovide accurate completion time estimates while marginally compromising onperformance and fairness.

Towards providing reliable job completion time predictions using PCS