Dynamic Computation Offloading in Edge Computing for Internet of Things

Nowadays, billions of Internet of Things (IoT) devices arise around us running complex and computation-intensive applications. Due to the limited resources of the IoT devices, it is appealing to offload the application tasks from IoT devices to the remote cloud data centers. However, offloading all the tasks to the cloud can put a significant burden on the network. One promising way to solve this issue is edge computing, where edge servers are provisioned at the network edge. In edge computing for IoT, as the task generating process is highly dynamic and the statistical information can hardly be obtained or precisely predicted, it is of great importance yet very challenging to effectively offload application tasks to achieve the tradeoff between offloading cost and performance. In this paper, we formulate the computation offloading as an optimization problem to minimize offloading cost while providing performance guarantees. Based on stochastic optimization, we propose a dynamic computation offloading algorithm (DCOA), which decomposes the optimization problem into a series of subproblems, and solves these subproblems concurrently in an online and distributed way. Theoretical analysis is presented which demonstrates that DCOA can achieve the tradeoff between offloading cost and performance. Experiments are also carried out to evaluate the effectiveness of DCOA.