Abstract
AbstractThe co-allocated data centers are to deploy online services and offline workloads in the same cluster to improve the utilization of resources. Spark application is a typical offline batch workload. At present, the resource scheduling strategy for co-allocated data centers mainly focuses on online services. Spark applications still use the original resource scheduling, which can’t solve the data dependency and deadline problems between spark applications and online services. This paper proposes a data-aware resource-scheduling model to meet the deadline requirement of Spark application and optimize the throughput of data processing on the premise of ensuring the quality of service of online services.
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