Improving spark application throughput via memory aware task co-location

Abstract

Data analytic applications built upon big data processing frameworks such as Apache Spark are an important class of applications. Many of these applications are not latency-sensitive and thus can run as batch jobs in data centers. By running multiple applications on a computing host, task co-location can significantly improve the server utilization and system throughput. However, effective task co-location is a non-trivial task, as it requires an understanding of the computing resource requirement of the co-running applications, in order to determine what tasks, and how many of them, can be co-located. State-of-the-art co-location schemes either require the user to supply the resource demands which are often far beyond what is needed; or use a one-size-fits-all function to estimate the requirement, which, unfortunately, is unlikely to capture the diverse behaviors of applications.