Cognitive Resource Optimization for the Decomposed Cloud Gaming Platform

Wei Cai, Chun Bun Henry Chan, Xiaofei Wang, Victor C.M. Leung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

18 Citations (Scopus)


Contrary to conventional gaming-on-demand services that stream gaming video from cloud to players' terminals, a decomposed cloud gaming platform supports flexible migrations of gaming components between the cloud server and the players' terminals. In this paper, we present the design and implementation of the proposed decomposed gaming system. The cognitive resource optimization of the system under distinct targets, including the minimization of cloud, network, and terminal resources and response delay, subject to quality of service (QoS) assurance, is formulated as a graph partitioning problem that is solved by exhaustive searches. Simulations and experimental results demonstrate the feasibility of cognitive resource management in a cloud gaming system to efficiently adapt to variations in the service environments, such as increasing the number of supported devices and reducing the network bandwidth consumption of user terminals, while satisfying different QoS requirements for gaming sessions. We also suggest two heuristic algorithms based on local greedy and genetic algorithm approaches, which can potentially provide scalable but suboptimal solutions in large-scale implementations.
Original languageEnglish
Article number7137648
Pages (from-to)2038-2051
Number of pages14
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number12
Publication statusPublished - 1 Dec 2015


  • Cloud
  • cognitive
  • decomposed
  • resource management
  • video game

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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