Lyapunov Optimization-Based Trade-Off Policy for Mobile Cloud Offloading in Heterogeneous Wireless Networks

In order to improve mobile users' service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud center...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on cloud computing Vol. 10; no. 1; pp. 491 - 505
Main Authors Li, Yun, Xia, Shichao, Zheng, Mengyan, Cao, Bin, Liu, Qilie
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 01.01.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Bandwidth
Cloud computing
Computation offloading
Delays
Electronic devices
heterogeneous wireless networks
Lyapunov optimization
Mobile cloud computing
Mobile computing
Mobile handsets
Optimization
Queues
Stochastic processes
Stochastic programming
Tradeoffs
Wireless fidelity
Wireless networks
Workload
Workloads
Online AccessGet full text

Cover

More Information
Summary:In order to improve mobile users' service experience, mobile cloud computing (MCC) is promoted. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates computing and storage overheads in cloud centers and bandwidth overhead on wireless links for offloading workloads of mobile users. Therefore, we should carefully design the offloading policy to decrease these overheads while easing the burdens of SMDs. To this end, we investigate the offloading policy in heterogeneous wireless networks. In this paper, a queue model is built to formulate the mobile users' workload offloading problem and Lyapunov optimization framework is proposed to make trade-off between system offloading utility and queue backlog. For deterministic WiFi connections, a Lagrangian optimization method is proposed to decide the optimal offloading workloads. Furthermore, considering random WiFi connection durations, a multi-stage stochastic programming method is proposed. The experimental results show effectiveness of the Lagrangian optimization offloading method for deterministic WiFi connection and the multi-stage stochastic programming method for random WiFi connection.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2019.2938504