Robust Workload and Energy Management for Sustainable Data Centers

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

Research output: Contribution to journalArticlepeer-review

69 Scopus citations


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
Issue number3
StatePublished - Mar 2016

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  • Cloud computing
  • data centers
  • energy storage


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