Distributed data processing systems like MapReduce, Spark, and Flink are popular tools for analysis of large datasets with cluster resources. Yet, users often overprovision resources for their data processing jobs, while the resource usage of these jobs also typically fluctuates considerably. Therefore, multiple jobs usually get scheduled onto the same shared resources to increase the resource utilization and throughput of clusters. However, job runtimes and the utilization of shared resources can vary significantly depending on the specific combinations of co-located jobs. This paper presents Hugo, a cluster scheduler that continuously learns how efficiently jobs share resources, considering metrics for the resource utilization and interference among co-located jobs. The scheduler combines offline grouping of jobs with online reinforcement learning to provide a scheduling mechanism that efficiently generalizes from specific monitored job combinations yet also adapts to changes in workloads. Our evaluation of a prototype shows that the approach can reduce the runtimes of exemplary Spark jobs on a YARN cluster by up to 12.5%, while resource utilization is increased and waiting times can be bounded.

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Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation to servers and hosted applications, testing them has been challenging with no available simulation platform that enables such testing for different scenarios and configurations. To facilitate evaluation and comparison of such techniques and algorithms, we introduce a simulation model for Quality-of-Service aware power budgeting and its implementation in CloudSim. We validate the proposed simulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.

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