Dynamic Service Placement for Virtual Reality Group Gaming on Mobile Edge Cloudlets

• Published in: IEEE journal on selected areas in communications Vol. 37; no. 8; pp. 1881 - 1897
• Main Authors: Zhang, Yuan, Jiao, Lei, Yan, Jinyao, Lin, Xiaojun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Customer relationship management; Delays; Functions (mathematics); Games; graph cut; Graph theory; group gaming; mobile edge computing; model predictive control; Operating costs; Placement; Polynomials; Predictive control; Provisioning; Rendering; Rendering (computer graphics); service placement; Switches; Virtual reality; Windows; Wireless networks
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2019.2927071

To realize mobile virtual reality (VR) group gaming services which are currently hampered by the prohibitive bandwidth and the stringent delay requirements, we investigate the problem of provisioning such services using the emerging mobile edge cloudlet (MEC) networks with a distributed content rendering architecture. The underlying dynamic rendering-module placement problem requires to optimize the service's operational cost and the users' end-to-end performance, involving multiple intertwined conflicting system objectives that are discrete, nonconvex, and higher degree polynomial functions with coupled decisions and arbitrary user dynamics over time. We solve this online placement problem by leveraging model predictive control (MPC) and overcoming the aforementioned challenges over each prediction window. We explore the connection between the placement problem and the minimal <inline-formula> <tex-math notation="LaTeX">s </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">t </tex-math></inline-formula> cut problem in graph theory and solve the former via solving a series of instances of the latter. We formally prove the performance guarantee of our approach. We also conduct extensive trace-driven evaluations and demonstrate the superior practical performance of our MPC-based approach compared to the de facto practices and the state-of-the-art alternatives.