DGLB: Distributed stochastic geographical load balancing over cloud networks

Tianyi Chen, Antonio G. Marques, Georgios B. Giannakis

Research output: Contribution to journalArticle

25 Scopus citations


Contemporary cloud networks are being challenged by the rapid increase of user demands and growing concerns about global warming, due to their substantial energy consumption. This requires future data centers to be both energy efficient and sustainable, which calls for leveraging cutting-edge features and the flexibility provided by the modern smart grids. To fulfill those goals, this paper puts forward a systematic approach to designing energy-aware traffic-efficient geographical load balancing schemes for data-center networks that are not only optimal, but also computationally efficient and amenable to distributed implementation. Under this comprehensive approach, workload and power balancing schemes are designed jointly across the network, both delay-tolerant and interactive workloads are accommodated, novel smart-grid features such as energy storage units are incorporated to cope with renewables, and incentive pricing mechanisms are adopted in the design. To further account for the spatio-temporal variation of demands, energy prices and renewables, the task is formulated as a two-timescale stochastic optimization. Leveraging dual stochastic approximation and the fast iterative shrinkage-thresholding algorithm (FISTA), the proposed optimization is decomposed across time slots (first-stage) and data centers (second-stage). While the resultant online algorithm is strictly feasible and provably optimal under a Markovian assumption for the underlying random processes, extensive numerical tests further demonstrate that it also works well in real-data scenarios, where the underlying randomness is highly correlated across time.

Original languageEnglish (US)
Article number7775032
Pages (from-to)1866-1880
Number of pages15
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number7
StatePublished - Jul 2017



  • Data center
  • Energy storages
  • Incentive payment
  • Network resource allocation
  • Renewables
  • Stochastic programming

Cite this