Reliable Function Computation Offloading in Cloud-Edge Collaborative Network

Shaonan Li, Yongqiang Xie, Zhongbo Li, Jin Qi, Yumeng Tian

Abstract: In this paper, we focus on the cloud-edge collaborative network, where a task is decomposed into a set of functions and could be offloaded to different computing nodes, which is referred to as Function Computation Offloading (FCO). One of the most important problems in FCO is to schedule the functions in computing nodes to achieve low latency and high reliability. We formulate FCO scheduling in the Cloud-edge Collaborative Network as mixed-integer nonlinear programming. The objective is to minimise the end-to-end delay of a task while satisfying the latency and reliability constraints. To solve the problem, we propose an efficient mechanism to decide the redundancy of functions according to the reliability requirements. Then, we deploy the non-redundant functions on the computing nodes. Finally, we present a Reinforcement Learning (RL) to learn the scheduling policy of the redundant functions to further reduce the end-to-end delay of the task. Simulation results show that our proposed algorithm can significantly reduce tasks’ completion time by about 13–26% with fewer iterations compared with other alternatives.