Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61109
Title: Cognitive resource optimization for the decomposed cloud gaming platform
Authors: Cai, W
Chan, HCB 
Wang, X
Leung, VCM
Keywords: Cloud
Cognitive
Decomposed
Resource management
Video game
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on circuits and systems for video technology, 2015, v. 25, no. 12, 7137648, p. 2038-2051 How to cite?
Journal: IEEE transactions on circuits and systems for video technology 
Abstract: 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.
URI: http://hdl.handle.net/10397/61109
ISSN: 1051-8215 (print)
1558-2205 (online)
DOI: 10.1109/TCSVT.2015.2450171
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