SDN Assisted Mobile Edge Computing for Collaborative Computation Offloading in Industrial Internet of Things
IEEE Internet of Things Journal
Mobile edge computing (MEC) can provision augmented computational capacity in proximity so as to better support Industrial Internet of Things (IIoT). Tasks from the IIoT devices can be outsourced and executed at the accessible computational access point (CAP). This computing paradigm enables the computing resources much closer to the IIoT devices, and thus satisfy the stringent latency requirement of the IIoT tasks. However, existing works in MEC that focus on task offloading and resource allocation seldom consider the load balancing issue. Therefore, load balance aware task offloading strategies for IIoT devices in MEC are urgently needed. In this paper, Software Defined Network (SDN) technology is adopted to address this issue, since the rule-based forwarding policy in SDN can help determine the most suitable offloading path and CAP for undertaking the computation. To this end, we formulate an optimization problem to minimize the response latency in the proposed SDN assisted MEC architecture. A greedy algorithm is put forward to obtain the approximate optimal solution in polynomial time. Simulation has been carried out to evaluate the performance of the proposed approach. The simulation results reveal that our approach outstands other approaches in terms of the response latency. IEEE
computation offloading, Computer architecture, IIoT, Industrial Internet of Things, load balancing, MEC, Optimization, Quality of service, response latency optimization, Robot sensing systems, Routing, SDN, Task analysis
C. Tang, C. Zhu, N. Zhang, M. Guizani and J. J. P. C. Rodrigues, "SDN Assisted Mobile Edge Computing for Collaborative Computation Offloading in Industrial Internet of Things," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3190281.