Scheduling of time-varying workloads using reinforcement learning

Shanka Subhra Mondal, Nikhil Sheoran, Subrata Mitra

Abstract:

Resource usage of production workloads running on shared
compute clusters often fluctuate significantly across time.
While simultaneous spike in the resource usage between two
workloads running on the same machine can create performance degradation, unused resources in a machine results
in wastage and undesirable operational characteristics for a
compute cluster. Prior works did not consider such temporal
resource fluctuations or their alignment for scheduling decisions. Due to the variety of time-varying workloads and their
complex resource usage characteristics, it is challenging to
design well-defined heuristics for scheduling them optimally
across different machines in a cluster. In this paper, we propose a Deep Reinforcement Learning (DRL) based approach
to exploit various temporal resource usage patterns of timevarying workloads as well as a technique for creating equivalence classes among a large number of production workloads
to improve scalability of our method. Validations with real
production traces from Google and Alibaba show that our
technique can significantly improve metrics for operational
excellence (e.g. utilization, fragmentation, resource exhaustion etc.) for a cluster compared to the baselines.