Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141655
Title: Scalable traffic management for mobile cloud services in 5G networks
Authors: Liu, Lanchao
Niyato, Dusit
Wang, Ping
Han, Zhu
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Liu, L., Niyato, D., Wang, P., & Han, Z. (2018). Scalable traffic management for mobile cloud services in 5G networks. IEEE Transactions on Network and Service Management, 15(4), 1560-1570. doi:10.1109/TNSM.2018.2867019
Journal: IEEE Transactions on Network and Service Management
Abstract: Mobile cloud computing has been introduced to improve the performance of mobile application clients by offloading data processing and storage to cloud. By deploying the service on several cloud-enabled data centers, the service provider can optimally locate service instances on the cloud to provide qualified services at a reasonable cost. However, a centralized approach for both request allocation and response routing does not scale efficiently due to a large number of mobile clients involved in the mobile service traffic management. Moreover, the random and unpredictable wireless network performance (e.g., delay) complicates the problem further. In this paper, we present a stochastic distributed optimization framework for mobile cloud traffic management in 5G networks. The framework takes the impact of random wireless network characteristics into account. Utilizing the alternating direction method of multipliers, the optimization problem is decomposed into independent subproblems, which are solved in a parallel fashion on distributed agents and coordinated through dual variables. The convergence issue under the stochastic setting is addressed, and the numerical tests validate the effectiveness of the proposed algorithm.
URI: https://hdl.handle.net/10356/141655
ISSN: 1932-4537
DOI: 10.1109/TNSM.2018.2867019
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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