Preemptive, Low Latency Datacenter Scheduling via Lightweight Virtualization

Data centers are evolving to host heterogeneous workloads on shared clusters to reduce the operational cost and achieve higher resource utilization. However, it is challenging to schedule heterogeneous workloads with diverse resource requirements and QoS constraints. On the one hand, latency-critical jobs need to be scheduled as soon as they are submitted to avoid any queuing delays. On the other hand, best-effort long jobs should be allowed to occupy the cluster when there are idle resources to improve cluster utilization. The challenge lies in how to minimize the queuing delays of short jobs while maximizing cluster utilization. Existing solutions either forcibly kill long jobs to guarantee low latency for short jobs or disable preemption to optimize utilization. Hybrid approaches with resource reservations have been proposed but need to be tuned for specific workloads.

In this paper, we propose and develop BIG-C, a container-based resource management framework for Big Data cluster computing. The key design is to leverage lightweight virtualization, a.k.a, containers to make tasks preemptable in cluster scheduling. We devise two types of preemption strategies: immediate and graceful preemptions and show their effectiveness and tradeoffs with loosely-coupled MapReduce workloads as well as iterative, in-memory Spark workloads. Based on the mechanisms for task preemption, we further develop a preemptive fair share cluster scheduler. We have implemented BIG-C in YARN. Our evaluation with synthetic and production workloads shows that low-latency and high utilization can be both attained when scheduling heterogeneous workloads on a contended cluster.