Maximizing the Quality of User Experience of Using Services in Edge Computing for Delay-Sensitive IoT Applications

The Internet of Things (IoT) technology offers unprecedented opportunities to interconnect human beings. However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices prevents users from experiencing high efficiency and seamless user experience. To address these shortcomings, the integrated MEC with remote clouds is a promising platform, where edge-clouds (cloudlet) are co-located with wireless access points in the proximity of IoT devices, thus intensive-computation and sensing data from IoT devices can be offloaded to the MEC network for processing, and the service response latency can be significantly reduced. In this paper, we study delay-sensitive service provisioning in an MEC network for IoT applications. We first formulate two novel optimization problems, i.e., the total utility maximization problems under both static and dynamic offloading task request settings, with the aim to maximize the accumulative user satisfaction of using the services provided by the MEC. We then show that the defined problems are NP-hard. We instead devise efficient approximation and online algorithms with provable performance guarantees for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising.