Evaluating job packing in warehouse-scale computing
Venue
IEEE Cluster, Madrid, Spain (2014)
Publication Year
2014
Authors
Abhishek Verma, Madhukar Korupolu, John Wilkes
BibTeX
Abstract
One of the key factors in selecting a good scheduling algorithm is using an
appropriate metric for comparing schedulers. But which metric should be used when
evaluating schedulers for warehouse-scale (cloud) clusters, which have machines of
different types and sizes, heterogeneous workloads with dependencies and
constraints on task placement, and long-running services that consume a large
fraction of the total resources? Traditional scheduler evaluations that focus on
metrics such as queuing delay, makespan, and running time fail to capture important
behaviors – and ones that rely on workload synthesis and scaling often ignore
important factors such as constraints. This paper explains some of the complexities
and issues in evaluating warehouse scale schedulers, focusing on what we find to be
the single most important aspect in practice: how well they pack long-running
services into a cluster. We describe and compare four metrics for evaluating the
packing efficiency of schedulers in increasing order of sophistication: aggregate
utilization, hole filling, workload inflation and cluster compaction.
