Distributed Data Center Bandwidth Allocation for Cloud-Based Streaming

Fanxin Kong, Xingjian Lu, Xue Liu

Research output: Contribution to journalArticlepeer-review


Cloud-based video streaming systems such as YouTube and Netflix are usually supported by the content delivery networks and data centers that can consume many megawatts of power. Most existing work independently studies the issues of improving quality of experience (QoE) for viewers and reducing the cost and emissions associated with the enormous energy usage of data centers. By contrast, this paper addresses them both, and jointly optimizes the QoE, the energy cost and emissions by intelligently allocating data center bandwidth among different client groups. Specially, we propose a distributed algorithm to achieve the optimal bandwidth allocation, given the prediction of future workload. The algorithm novelly decomposes the optimization process into separate ones, which are solved iteratively across data centers and clients. Further, the algorithm has robust performance guarantee in terms of the variance of the prediction error. We demonstrate its convergence and robustness by both proofs using theoretical analysis and validation based on trace-driven simulations. The results further show that the proposed algorithm converges very fast and achieves much better QoE-cost balance than existing approaches.

Original languageEnglish (US)
Article number7973105
Pages (from-to)263-276
Number of pages14
JournalIEEE Transactions on Sustainable Computing
Issue number2
StatePublished - Apr 1 2019
Externally publishedYes


  • Cloud-based streaming
  • bandwidth allocation
  • data centers
  • distributed algorithms
  • dynamic streaming
  • energy cost
  • quality of experience

ASJC Scopus subject areas

  • Software
  • Renewable Energy, Sustainability and the Environment
  • Hardware and Architecture
  • Control and Optimization
  • Computational Theory and Mathematics


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