Optimizing energy and performance for server-class file system workloads

Author:

Sehgal Priya1,Tarasov Vasily1,Zadok Erez1

Affiliation:

1. Stony Brook University, Stony Brook, NY

Abstract

Recently, power has emerged as a critical factor in designing components of storage systems, especially for power-hungry data centers. While there is some research into power-aware storage stack components, there are no systematic studies evaluating each component's impact separately. Various factors like workloads, hardware configurations, and software configurations impact the performance and energy efficiency of the system. This article evaluates the file system's impact on energy consumption and performance. We studied several popular Linux file systems, with various mount and format options, using the FileBench workload generator to emulate four server workloads: Web, database, mail, and fileserver, on two different hardware configurations. The file system design, implementation, and available features have a significant effect on CPU/disk utilization, and hence on performance and power. We discovered that default file system options are often suboptimal, and even poor. In this article we show that a careful matching of expected workloads and hardware configuration to a single software configuration—the file system—can improve power-performance efficiency by a factor ranging from 1.05 to 9.4 times.

Funder

Division of Computing and Communication Foundations

Publisher

Association for Computing Machinery (ACM)

Subject

Hardware and Architecture

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Optimizing Energy-Performance Trade-Offs in Solar-Powered Edge Devices;Proceedings of the 2018 ACM/SPEC International Conference on Performance Engineering;2018-03-30

2. On Space Utilization Enhancement of File Systems for Embedded Storage Systems;ACM Transactions on Embedded Computing Systems;2017-07-07

3. Energy-aware processing of big data in homogeneous cluster;Signal, Image and Video Processing;2016-09-08

4. Energy--Performance Trade-Offs via the EP Queue;ACM Transactions on Modeling and Performance Evaluation of Computing Systems;2016-06-07

5. Power Reduction Techniques in Cloud Data Centers;Advanced Materials Research;2014-12

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