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


Mobile Cloud Computing, also known as MCC, is gaining popularity as a means of enhancing the overall service experience for mobile users. Although MCC can alleviate the burdens of Smart mobile devices (SMDs) by offloading computation-intensive applications to the cloud, it also aggravates the computing and storage overheads in cloud centers as well as the bandwidth overhead on wireless links for offloading the workloads of mobile users. This is because MCC is used to offload the workloads of mobile users. As a result, we ought to approach the formulation of the offloading policy with great deliberation in order to cut down on these overhead costs while simultaneously relieving the pressures placed on SMDs. In order to achieve this goal, we look into the offloading policy used in different kinds of wireless networks. In this article, a queue model is constructed in order to formulate the problem of offloading the workload of mobile users, and a Lyapunov optimization framework is proposed in order to make a trade-off between the utility of system offloading and the backlog of the queue. A Lagrangian optimization method is proposed as a means of determining the most effective method of offloading workloads for deterministic WiFi connections. In addition, taking into account the unpredictability of the lengths of WiFi connections, a multi-stage stochastic programming method is suggested. The results of the experiments demonstrate that the multi-stage stochastic programming method and the Lagrangian optimization offloading method are effective approaches for establishing random and deterministic WiFi connections respectively.

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