Revenue driven virtual machine management in green datacenter networks towards big data

Liang Zhang, Tao Han, Nirwan Ansari

Research output: Contribution to journalConference articlepeer-review

6 Scopus citations


The big data era is presenting unprecedented opportunities for generating new revenues in various sectors ranging from health care, economics, life science, to manufacturing. Datacenters (DCs) are widely deployed to provision various application services as well as to process big data. Since more and more servers are installed in DCs, the cost of electricity incurs a financial burden for the DC operators. Many DCs are equipped with renewable energy to reduce the electricity bill. However, the locations of the energy demands do not match the locations of the renewable energy generation. This mismatch may be addressed by virtual machine (VM) migration. The DCs, which lack renewable energy, can migrate their workloads to other DCs, which have abundant renewable energy. In addition, DC operators always want to maximize revenue and minimize operation cost. In this paper, the problem of maximizing revenue and minimizing operation cost of a green DC network enabled with and without VM migration is formulated by integer linear programming. Simulation results show that optimal results can be reached for small size problems. Two heuristic algorithms are proposed to efficiently solve large size problems. To our best knowledge, this is the first study of the revenue driven VM management problem in green DC networks towards big data with VM migration.

Original languageEnglish (US)
Article number7842228
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - 2016
Event59th IEEE Global Communications Conference, GLOBECOM 2016 - Washington, United States
Duration: Dec 4 2016Dec 8 2016

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


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