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Due to the diversity in the applications that run in clusters, many different application frameworks have been developed, such as MapReduce for data-intensive batch jobs and Spark for interactive data analytics. A framework is first deployed in a cluster, and then starts executing a large set of jobs that are submitted over time. When multiple such frameworks with time-varying resource demands are presentin a single cluster, static allocation of resources on a per-framework basis leads to low system utilization and resource fragmentation. In this paper, we present koala-f, a resource manager that dynamically provides resources to frameworks by employing a feedback loop to collecttheir possibly different performance metrics. Frameworks periodically -- not necessarily with the same frequency -- report the values of their performancemetrics to koala-f, which then either rebalances their resources individuallyagainst the idle-resource pool, or, when the latter is empty, rebalances their resources amongst them. We demonstrate the effectiveness of koala-f with experiments in a real system.
Original languageEnglish
Title of host publication16th IEEE/ACM Int'l Symp. on Cluster, Cloud and Grid Computing (CCGrid)
PublisherIEEE
Pages80
Number of pages89
DOIs
StatePublished - 2016

ID: 29617618