Phoenix: Constraint aware scheduling for heterogeneous datacenters
Prashanth Thinakaran, Jashwant Raj Gunasekaran, Bikash Sharma, Mahmut Kandemir and Chita Das
Pennsylvania State University, Pennsylvania State University, Microsoft Corp, Pennsylvania State University, Pennsylvania State University

Today’s datacenters are increasingly becoming diverse regarding both hardware and software architectures in order to support a myriad of applications. These applications are also heterogeneous in terms of job response times and resource requirements (eg., Number of Cores, GPUs, Network Speed) and they are expressed as task constraints. Constraints are used for ensuring task performance guarantees/Quality of Service(QoS) by enabling the application to express its specific resource requirements. While several schedulers have recently been proposed that aim to improve overall application and system performance, few of these schedulers consider resource constraints across tasks while making the scheduling decisions. Furthermore, latency-critical workloads and short-lived jobs that typically constitute about 90% of the total jobs in a datacenter have strict QoS requirements, which can be mitigated by minimizing the tail latency through effective scheduling. In this paper, we propose Phoenix, a constraint-aware hybrid scheduler to address both these problems (constraint awareness and ensuring low tail latency) by minimizing the job response times at constrained workers and proactively reordering the tasks. We use a novel Constraint Resource Vector (CRV) based scheduling, which in turn facilitates reordering of the jobs in a queue to minimize tail latency. We have used the publicly available Google traces to analyze their constraint characteristics and have embedded these constraints in Cloudera and Yahoo cluster traces for studying the impact of traces on system performance. Experiments with Google, Cloudera and Yahoo cluster traces across 15,000 worker node cluster shows that Phoenix improves the 99th percentile job response times on an average by 1.9x across all three traces when compared against a state-of-the-art hybrid scheduler. Further, in comparison to other distributed scheduler like Hawk, it improves the 90th and 99th percentile job response times by 4.5x and 5x respectively.