QoS-Aware Task Scheduling in Cloud-Edge Environment

Since the limitations of the user equipment, they do not have enough computing power to process large amounts of data. At the same time, it's unbearable for users to spend a long time uploading data to the remote cloud center. In order to solve these problems above, the concept of mobile edge computing(MEC) is proposed. The computing and storage resources are placed close to the end equipment, reducing the transmission delay. MEC can meet the high real-time requirements of the user equipment. In the real-time face recognition application scenario, a three-layer hierarchy MS-CE is proposed for the shortcoming of the traditional centralized cloud center. And the distributed MEC servers are utilized at the MEC layer to provide parallel computing capabilities. Aiming at the problem of how to perform task scheduling in a geographically distributed MEC server, we propose a task scheduling based queue algorithm(TSBQ), which considers the data transmission delay and server load, and carries out a reasonable task allocation policy. We evaluate the MS-CE and TSBQ through simulation experiments. We can find the MS-CE architecture are better than others and TSBQ is more effective than Corral and Greedy.