Author ORCID Identifier

0000-0001-8636-4662

Document Type

Thesis

Date of Award

Spring 5-20-2018

Keywords

energy power heterogeneous cluster mesos scheduling distributed systems RAPL

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Madhusudhan Govindaraju

Subject Heading(s)

energy power heterogeneous cluster mesos scheduling distributed systems RAPL; OS and Networks; Systems Architecture

Abstract

As data centers continue to grow in scale, the resource management software needs to work closely with the hardware infrastructure to provide high utilization, performance, fault tolerance, and high availability. Apache Mesos has emerged as a leader in this space, providing an abstraction over the entire cluster, data center, or cloud to present a uniform view of all the resources. In addition, frameworks built on Mesos such as Apache Aurora, developed within Twitter and later contributed to the Apache Software Foundation, allow massive job submissions with heterogeneous resource requirements. The availability of such tools in the Open Source space, with proven record of large-scale production use, make them suitable for research on how they can be adapted for use in campus-clusters and emerging cloud infrastructures for different workloads in both academia and industry. As data centers run these workloads and strive to maintain high utilization of their components, they suffer a significant cost in terms of energy and power consumption. To address this cost we have developed our own framework, Electron, for use with Mesos. Electron is designed to be configurable with heuristic-driven power capping policies along with different scheduling policies such as Bin Packing and First Fit. We characterize the performance of Electron, in comparison with the widely used Aurora framework. On average, our experiments show that Electron can reduce the 95th percentile of CPU and DRAM power usage by 27.89%, total energy consumption by 19.15%, average power consumption by 27.90%, and max peak power usage by 16.91%, while maintaining a similar makespan when compared to Aurora using the proper combination of power capping and scheduling policies.

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