On the Performance of SMASH: A Non-Preemptive Window-Based Scheduler for Multiserver Jobs

Abstract

The efficient execution of data center jobs that require simultaneous use of different resource types is of critical importance. When processing capacity is the crucial resource for jobs execution, the locution multiserver jobs is used, where the term server indicates processors or CPU cores providing processing capacity. Each multiserver job carries a requirement expressed in number of servers it requires to run, and service duration. Achieving efficient execution of multiserver jobs relies heavily on effective scheduling of jobs on the existing servers. Several schedulers have been proposed, aimed at improving resource utilization, at the cost of increased complexity. Due to the limited availability of theoretical results on scheduler behavior in the case of multiserver jobs, data center schedulers are often designed based only on managers’ experience. In this article, aiming to expand the understanding of the multiserver job schedulers’ performance, we study Small Shuffle (SMASH) schedulers, a class of nonpreemptive, service time oblivious, window-based multiserver job scheduling algorithms that strike a balance between simplicity and efficient resource utilization, while allowing performance evaluation in simpler settings. SMASH implies only a marginal increase in complexity compared to FIFO, yet it delivers substantial performance improvements for multiserver jobs. Depending on the system parameters, SMASH can nearly double the system’s stability region with respect to FIFO, leading to significantly lower response times across a broad region of loads. Moreover, the magnitude of this improvement scales with the chosen window size, allowing performance to be tuned to the system’s operating conditions. We first study the capacity of SMASH with analytical tools in simple settings, then we investigate the performance of SMASH and other schedulers with simulations under more realistic workloads, designed with parameters derived from measurements of real data centers. Results show that SMASH offers a very good compromise between performance and complexity.