An experimental evaluation of datacenter workloads on low-power embedded micro servers

Yiran Zhao, Shen Li, Shaohan Hu, Hongwei Wang, Shuochao Yao, Huajie Shao, Tarek Abdelzaher

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

This paper presents a comprehensive evaluation of an ultra- low power cluster, built upon the Intel Edison based micro servers. The improved performance and high energy effi- ciency of micro servers have driven both academia and in- dustry to explore the possibility of replacing conventional brawny servers with a larger swarm of embedded micro ser- vers. Existing attempts mostly focus on mobile-class mi- cro servers, whose capacities are similar to mobile phones. We, on the other hand, target on sensor-class micro servers, which are originally intended for uses in wearable technolo- gies, sensor networks, and Internet-of-Things. Although sensor-class micro servers have much less capacity, they are touted for minimal power consumption (< 1 Watt), which opens new possibilities of achieving higher energy efficiency in datacenter workloads. Our systematic evaluation of the Edison cluster and comparisons to conventional brawny clus- ters involve careful workload choosing and laborious param- eter tuning, which ensures maximum server utilization and thus fair comparisons. Results show that the Edison clus- ter achieves up to 3:5× improvement on work-done-per-joule for web service applications and data-intensive MapReduce jobs. In terms of scalability, the Edison cluster scales lin- early on the throughput of web service workloads, and also shows satisfactory scalability for MapReduce workloads de- spite coordination overhead.

Original languageEnglish (US)
Title of host publicationProceedings of the VLDB Endowment
PublisherAssociation for Computing Machinery
Pages696-707
Number of pages12
Edition9
DOIs
StatePublished - 2016
Event42nd International Conference on Very Large Data Bases, VLDB 2016 - Delhi, India
Duration: Sep 5 2016Sep 9 2016

Publication series

NameProceedings of the VLDB Endowment
Number9
Volume9
ISSN (Electronic)2150-8097

Other

Other42nd International Conference on Very Large Data Bases, VLDB 2016
CountryIndia
CityDelhi
Period9/5/169/9/16

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Computer Science(all)

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