A pricing strategy for job allocation in mobile grids using a non-cooperative bargaining theory framework

  • Authors:

  • Preetam Ghosh;Nirmalya Roy;Sajal K. Das;Kalyan Basu

  • Affiliations:

  • Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019-0015, USA;Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019-0015, USA;Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019-0015, USA;Center for Research in Wireless Mobility and Networking (CReWMaN), Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019-0015, USA

  • Venue:
  • Journal of Parallel and Distributed Computing - Special issue: Design and performance of networks for super-, cluster-, and grid-computing: Part II
  • Year:
  • 2005

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Abstract

Due to their inherent limitations in computational and battery power, storage and available bandwidth, mobile devices have not yet been widely integrated into grid computing platforms. However, millions of laptops, PDAs and other portable devices remain unused most of the time, and this huge repository of resources can be potentially utilized, leading to what is called a mobile grid environment. In this paper, we propose a game theoretic pricing strategy for efficient job allocation in mobile grids. By drawing upon the Nash bargaining solution, we show how to derive a unified framework for addressing such issues as network efficiency, fairness, utility maximization, and pricing. In particular, we characterize a two-player, non-cooperative, alternating-offer bargaining game between the Wireless Access Point Server and the mobile devices to determine a fair pricing strategy which is then used to effectively allocate jobs to the mobile devices with a goal to maximize the revenue for the grid users. Simulation results show that the proposed job allocation strategy is comparable to other task allocation schemes in terms of the overall system response time.