Interference-aware Workload Scheduling in Co-located Data Centers

Abstract

AbstractModern data centers typically contain thousands of servers, providing various computing and storage services for users. The strategy to provide reliable and high-performance online services is to over-allocate resources for online services, which results in a waste of cluster resources. Therefore, cloud vendors tend to co-locate online services and offline batch jobs into the same cluster to improve resource utilization. However, the co-location leads to contention on shared resources and causes mutual performance interference, which may degrade the QoS (Quality of Service) of online services. We present a performance interference model based on linear regression to predict the performance interference. Furthermore, the model can perceive the status of servers in real-time for more refined and accurate prediction. Then, we design an interference-aware workload scheduling strategy that can schedule batch jobs to the server while introducing minimal interference. The evaluation demonstrates that our scheduling strategy can at best increase the throughput of batch jobs by 48.95% and 27.09% compared with round-robin scheduling and random scheduling while guaranteeing the QoS of online services. The paper aims to provide some elements of answering the following general question: How do we design cloud systems and data centers scheduling strategies that can withstand the human pressure of global-scale use and still provide robust and secure services to end-users?KeywordsCloud computingWorkload co-locationPerformance interferencePerformance predictionWorkload schedulingData centers