CWS: a model-driven scheduling policy for correlated workloads

We define CWS, a non-preemptive scheduling policy for workloads with correlated job sizes. CWS tackles the scheduling problem by inferring the expected sizes of upcoming jobs based on the structure of correlations and on the outcome of past scheduling decisions. Size prediction is achieved using a class of Hidden Markov Models (HMM) with continuous observation densities that describe job sizes. We show how the forward-backward algorithm of HMMs applies effectively in scheduling applications and how it can be used to derive closed-form expressions for size prediction. This is particularly simple to implement in the case of observation densities that are phase-type (PH-type) distributed, where existing fitting methods for Markovian point processes may also simplify the parameterization of the HMM workload model. Based on the job size predictions, CWS emulates size-based policies which favor short jobs, with accuracy depending mainly on the HMM used to parametrize the scheduling algorithm. Extensive simulation and analysis illustrate that CWS is competitive with policies that assume exact information about the workload.