PSBS: Practical Size-Based Scheduling

Abstract

Size-based schedulers have very desirable performance properties: optimal or near-optimal response time can be coupled with strong fairness. Despite this, however, such systems are rarely implemented in practical settings, because they require knowing a priori the amount of work needed to complete jobs: this assumption is difficult to satisfy in concrete systems. It is definitely more likely to inform the system with an estimate of the job sizes, but existing studies point to somewhat pessimistic results if size-based policies use imprecise job size estimations. We take the goal of designing scheduling policies that explicitly deal with inexact job sizes . First, we prove that, in the absence of errors, it is always possible to improve any scheduling policy by designing a size-based one that dominates it: in the new policy, no jobs will complete later than in the original one. Unfortunately, size-based schedulers can perform badly with inexact job size information when job sizes are heavily skewed; we show that this issue, and the pessimistic results shown in the literature, are due to problematic behavior when large jobs are underestimated. Once the problem is identified, it is possible to amend size-based schedulers to solve the issue. We generalize FSP—a fair and efficient size-based scheduling policy—to solve the problem highlighted above; in addition, our solution deals with different job weights (that can be assigned to a job independently from its size). We provide an efficient implementation of the resulting protocol, which we call Practical Size-Based Scheduler (PSBS). Through simulations evaluated on synthetic and real workloads, we show that PSBS has near-optimal performance in a large variety of cases with inaccurate size information, that it performs fairly and that it handles job weights correctly. We believe that this work shows that PSBS is indeed pratical, and we maintain that it could inspire the design of schedulers in a wide array of real-world use cases.