Computation Offloading towards Edge Computing

Abstract

We are living in a world where massive end devices perform computing everywhere and every day. However, these devices are constrained by the battery and computational resources. With the increasing number of intelligent applications (e.g., augmented reality and face recognition) which require much more computational power, they shift to perform computation offloading to the cloud, known as mobile cloud computing (MCC). Unfortunately, the cloud is usually far away from end devices, leading to a high latency as well as the bad quality of experience (QoE) for latency-sensitive applications. In this context, the emergence of edge computing is no coincidence. Edge computing extends the cloud to the edge of the network, close to end users, bringing ultra-low latency and high bandwidth. Consequently, there is a trend of computation offloading towards edge computing. In this paper, we provide a comprehensive perspective on this trend. Firstly, we give an insight into the architecture refactoring in edge computing. Based on that insight, the paper reviews the state of the art research on computation offloading, in terms of application partitioning, task allocation, resource management, and distributed execution, with highlighting features for edge computing. Then, we illustrate some disruptive application scenarios which we envision as critical drivers for the flourish of edge computing, such as real-time video analytics, smart “things” (e.g., smart city and smart home), vehicle applications, and cloud gaming. Finally, we discuss opportunities and future research directions.