Performance Analysis and Optimization of Delayed Offloading System With Opportunistic Fog Node

Abstract

Fog node (FN) close to Internet of Things (IoT) devices can be exploited to offload the computing task of IoT devices. However, when lots of tasks are simultaneously offloaded from multiple IoT devices, FN can be overloaded. To mitigate this problem, we introduce a DYSON: DelaYed offloading System with Opportunistic fog Node (OFN) such as vehicles and mobile phones which can opportunistically reduce the load of static FN thanks to their mobility. In DYSON, to maximize the offloading effect of OFN, when IoT device cannot use OFN, it delays the task offloading with the expectation of future contacts of OFN. To assess the performance of DYSON, we develop an analytic model for the opportunistic offloading probability that the task can be offloaded to OFN. Based on the analytic model, we derive the optimal delay timer to maximize the opportunistic offloading probability while maintaining the probability that the task cannot be processed within the deadline below a target probability. Extensive simulation results are provided to show the superiority of DYSON.