Task partitioning and offloading in IoT cloud-edge collaborative computing framework: a survey
Haiming Chen, Wei Qin, Lei Wang
Abstract
Abstract Internet of Things (IoT) is made up with growing number of facilities, which are digitalized to have sensing, networking and computing capabilities. Traditionally, the large volume of data generated by the IoT devices are processed in a centralized cloud computing model. However, it is no longer able to meet the computational demands of large-scale and geographically distributed IoT devices for executing tasks of high performance, low latency, and low energy consumption. Therefore, edge computing has emerged as a complement of cloud computing. To improve system performance, it is necessary to partition and offload some tasks generated by local devices to the remote cloud or edge nodes. However, most of the current research work focuses on designing efficient offloading strategies and service orchestration. Little attention has been paid to the problem of jointly optimizing task partitioning and offloading for different application types. In this paper, we make a comprehensive overview on the existing task partitioning and offloading frameworks, focusing on the input and core of decision engine of the framework for task partitioning and offloading. We also propose comprehensive taxonomy metrics for comparing task partitioning and offloading approaches in the IoT cloud-edge collaborative computing framework. Finally, we discuss the problems and challenges that may be encountered in the future.