3.8 Article

Bargaining Game-Based Scheduling for Performance Guarantees in Cloud Computing

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/3141233

Keywords

Cloud computing; cooperative game; Nash bargaining solution; performance guarantees

Funding

  1. National Key R&D Program of China [2016YFB0201402]
  2. National Natural Science Foundation of China [61702170, 61602350, 61602170, 61402400, 61370098, 61672219, 61772182, 61572176, L1624040]
  3. Key Program of National Natural Science Foundation of China [61432005]
  4. National Outstanding Youth Science Program of National Natural Science Foundation of China [61625202]
  5. National High-tech R&D Program of China [2015AA015305]
  6. Key Technology Research and Development Programs of Guangdong Province [2015B010108006]
  7. International S&T Cooperation Program of China [2015DFA11240]
  8. Chinese Postdoctoral Science Foundation [2016M602409, 2016M602410]

Ask authors/readers for more resources

In this article, we focus on request scheduling with performance guarantees of all users in cloud computing. Each cloud user submits requests with average response time requirement, and the cloud provider tries to find a scheduling scheme, i.e., allocating user requests to limited servers, such that the average response times of all cloud users can be guaranteed. We formulate the considered scenario into a cooperative game among multiple users and try to find a Nash bargaining solution (NBS), which can simultaneously satisfy all users' performance demands. We first prove the existence of NBS and then analyze its computation. Specifically, for the situation when all allocating substreams are strictly positive, we propose a computational algorithm (CA), which can find the NBS very efficiently. For the more general case, we propose an iterative algorithm (IA), which is based on duality theory. The convergence of our proposed IA algorithm is also analyzed. Finally, we conduct some numerical calculations. The experimental results show that our IA algorithm can find an appropriate scheduling strategy and converges to a stable state very quickly.

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