Robust Workload and Energy Management for Sustainable Data Centers

Tianyi Chen, Yu Zhang, Xin Wang, Georgios B Giannakis

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

A large number of geo-distributed data centers begin to surge in the era of data deluge and information explosion. To meet the growing demand in massive data processing, the infrastructure of future data centers must be energy-efficient and sustainable. Facing this challenge, a systematic framework is put forth in this paper to integrate renewable energy sources (RES), distributed storage units, cooling facilities, as well as dynamic pricing into the workload and energy management tasks of a data center network. To cope with RES uncertainty, the resource allocation task is formulated as a robust optimization problem minimizing the worst-case net cost. Compared with existing stochastic optimization methods, the proposed approach entails a deterministic uncertainty set where generated RES reside, thus can be readily obtained in practice. It is further shown that the problem can be cast as a convex program, and then solved in a distributed fashion using the dual decomposition method. By exploiting the spatio-temporal diversity of local temperature, workload demand, energy prices, and renewable availability, the proposed approach outperforms existing alternatives, as corroborated by extensive numerical tests performed using real data.

Original languageEnglish (US)
Article number7397855
Pages (from-to)651-664
Number of pages14
JournalIEEE Journal on Selected Areas in Communications
Volume34
Issue number3
DOIs
StatePublished - Mar 2016

Bibliographical note

Funding Information:
This work was supported in part by NSF Grant 1509040, Grant 1509005, Grant 1423316, Grant 1442686, and Grant 1202135.

Publisher Copyright:
© 1983-2012 IEEE.

Keywords

  • Cloud computing
  • data centers
  • energy storage

Fingerprint

Dive into the research topics of 'Robust Workload and Energy Management for Sustainable Data Centers'. Together they form a unique fingerprint.

Cite this