Cluster fair queueing: Speeding up data-parallel jobs with delay guarantees

Chen Chen, Wei Wang, Shengkai Zhang, Bo Li

Abstract: Cluster scheduler serves as a critical component to data-parallel systems in datacenters. Ideally, a scheduler should provide predictable performance with guarantees on the maximal job completion delay, while at the same time ensuring the minimal mean response time. Practically however, performance predictability and optimality are often conflicting with each other. The results often are a plethora of scheduling policies that either achieve predictable performance at the expense of long response times (e.g., max-min fairness), or run the risk of starving some jobs to obtain the minimal mean response time (e.g., Shortest Remaining Processing Time First). To address these problems, we develop a new scheduler, Cluster Fair Queueing (CFQ), which preferentially offers resources to jobs that complete the earliest under a fair sharing policy. We show that CFQ is able to minimize the mean response time while at the same time ensuring jobs to finish within a constant time after their completion under fair sharing. Our Spark deployment on a 100-node EC2 cluster demonstrates that compared to the built-in fair scheduler, CFQ can decrease the mean response time by 40%, which speeds up more than 40% of jobs by over 75% on average.