Abstract

Thanks to the recent rapid development of 5G technology, multimedia communication services, such as live streaming and short movies, have attracted a lot of attention and are now a big component of people’s daily social contacts. However, the growing demand for multimedia communication creates new challenges for the wireless network’s processing and transmission capabilities. Many believe that mobile edge computing (MEC) offers a possible solution to the problems listed above. A potential answer to capacity and performance issues in aging systems, such as Mobile Cloud Computing (MCC), has been identified as Mobile Edge Computing. In this study, we focus on task scheduling in a resource-constrained MEC network, where a number of base stations (BS), each with a MEC server, help a number of latency-sensitive user equipments (UEs) with their computation. By carefully planning the task scheduling between UEs and BSs, we hope to simultaneously reduce the system’s consumption energy and increase the number of offloaded tasks. In the IoT space, computation offloading is growing in popularity. Individual devices cannot meet the massive demand for computational resources. These days, several quantified QoS targets, including time, cost, energy, and bandwidth, are optimized to meet this issue. A thorough explanation of the numerous works that are planned to address the aforementioned problems is provided in this work.