Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90123
Title: The server allocation problem for session-based multiplayer cloud gaming
Authors: Deng, Yunhua
Li, Yusen
Seet, Ronald
Tang, Xueyan
Cai, Wentong
Keywords: Multiplayer Online Game
DRNTU::Engineering::Computer science and engineering
Cloud Gaming
Issue Date: 2018
Source: Deng, Y., Li, Y., Seet, R., Tang, X., & Cai, W. (2018). The Server Allocation Problem for Session-Based Multiplayer Cloud Gaming. IEEE Transactions on Multimedia, 20(5), 1233-1245. doi:10.1109/TMM.2017.2760621
Series/Report no.: IEEE Transactions on Multimedia
Abstract: Advances in cloud computing and GPU virtualization are allowing the game industry to move into a cloud gaming era. In this paper, we consider multiplayer cloud gaming (MCG), which is the natural integration of multiplayer online gaming and cloud gaming paradigms. With MCG, a game server and a set of rendering servers for the players need to be located and launched in the clouds for each game session. We formulate an MCG server allocation problem with the objective of minimizing the total server rental and bandwidth cost charged by the cloud to support an MCG session. The MCG server allocation problem is hard to solve optimally. We propose several efficient heuristics to address the problem and carry out theoretical analysis for the proposed hill-climbing algorithm. We conduct extensive experiments using real Internet latency and cloud pricing datasets to evaluate the effectiveness of our proposed algorithms as well as several alternatives. Experimental results show that our best algorithm can achieve near-optimal cost under real-time latency constraints.
URI: https://hdl.handle.net/10356/90123
http://hdl.handle.net/10220/48395
ISSN: 1520-9210
DOI: http://dx.doi.org/10.1109/TMM.2017.2760621
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TMM.2017.2760621.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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