Scheduling Job Streams on Uniprocessors with Cold Start Delays

We consider uniprocessor job scheduling where jobs have deadlines and each job belongs to a job family. Each job family has an associated setup time or cold start delay, and when a job is scheduled, if the predecessor job does not belong to the same job family then this setup time needs to be included. We examine the scheduling problem when the objective is to minimize the number of tardy jobs, and the challenge of including the setup time for switching between job families results in poor performance of well-known policies such as earliest deadline first (EDF). We propose a near-optimal online scheduling policy for jobs with deadlines, on uniprocessor platforms. This problem arises in a variety of contexts including serverless computing and MLaaS (machine learning as a service) where a job request may need a suitable resource container to be provisioned if an earlier request was not of the same type. The general offline problem of job scheduling with job families and setup costs has previously been studied and shown to be NP-Hard. In an effort to improve our understanding of the online problem with the objective of maximizing the number of jobs that meet their deadlines, we focus on the case where all jobs have the same execution time. We show that even this special case is NP-Hard in the offline setting. The policy we propose, which requires job buffering, is nearly 1-competitive when each job has a reasonably large slack. We also propose a heuristic that performs well in many situations despite a weak competitive ratio.