Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

Lin Gu, Deze Zeng, Song Guo, Shui Yu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results.
Original languageEnglish
Title of host publication2014 International Conference on Computing, Networking and Communications, ICNC 2014
PublisherIEEE Computer Society
Pages211-215
Number of pages5
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 International Conference on Computing, Networking and Communications, ICNC 2014 - Honolulu, HI, United States
Duration: 3 Feb 20146 Feb 2014

Conference

Conference2014 International Conference on Computing, Networking and Communications, ICNC 2014
CountryUnited States
CityHonolulu, HI
Period3/02/146/02/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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