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Software-Defined Networking Based Request Allocation in Distributed Datacenters

  • Heng Qi
  • Keqiu Li
Chapter
Part of the SpringerBriefs in Electrical and Computer Engineering book series (BRIEFSELECTRIC)

Abstract

Large-scale Internet applications, such as information retrieval or video streaming, are usually built on top of distributed datacenters. In these applications, the request allocation problem is a fundamental problem, aiming to efficiently allocate massive requests among distributed datacenters. Generally, there are two basic factors that should be considered. First, from an overall system perspective, service provider expects to achieve high bandwidth utilization and load balance. Second, from an individual perspective, end-users have a strong desire for good user experience and fair treatment. To the best of our knowledge, existing approaches solely focus on either the former or the latter. Software-defined networking (SDN) makes it possible to implement global optimization over an entire network consisting of distributed datacenters. Thus, an SDN controller can be used as the central portal to allocate requests, satisfying the needs of both service providers and end-users. To address this problem, we first develop a general formulation of the request allocation problem. Specifically, we guarantee the benefits of both the service providers and end-users, which are modeled by two Nash bargaining games. Then, we further present an efficient request allocation algorithm based on logarithmic smoothing. We theoretically prove that our request allocation algorithm significantly converges to a unique solution. Finally, we conduct a large number of experiments based on real-world traces. These simulation results demonstrate the efficiency of our request allocation algorithm.

Keywords

Service Provider Load Balance User Experience Bandwidth Utilization Nash Bargaining Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© The Author(s) 2016

Authors and Affiliations

  • Heng Qi
    • 1
  • Keqiu Li
    • 1
  1. 1.Dalian University of TechnologyDalianChina

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