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In this paper, we make a case for providing job completion time estimates to GPU cluster users, similar to providing 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 GPU schedulers 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., queue weights) that meets specific goals for predictability. It uses a simulation-aided search strategy to efficiently discover WFQ configurations that lie around the Pareto front of the trade-off space between these objectives. We implement and evaluate PCS in the context of scheduling ML training workloads 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. 

Cloud providers are highly incentivized to reduce latency. One way they do this is by locating data centers as close to users as possible. These “cloud edge” data centers are placed in metropolitan areas and enable edge computing for residents of these cities. Therefore, which cities are selected to host edge data centers determines who has the fastest access to applications requiring edge compute — creating a digital divide between those closest and furthest from the edge. In this study we measure latency to the current and predicted cloud edge of three major cloud providers around the world. Our measurements use the RIPE Atlas platform targeting cloud regions, AWS Local Zones, and network optimization services that minimize the path to the cloud edge. An analysis of the digital divide shows rising inequality as the relative difference between users closest and farthest from cloud compute increases. We also find this inequality unfairly affects lower income census tracts in the US. This result is extended globally using remotely sensed night time lights as a proxy for wealth. Finally, we demonstrate that low earth orbit satellite internet can help to close this digital divide and provide more fair access to the cloud edge.