Adaptive Performance Modeling Framework for QoS-Aware Offloading in MEC-Based IIoT Systems

The extensive growth in Industrial Internet of Things (IIoT) applications has tremendously increased the demands for low latency and resource-sensitive computing to accomplish critical industrial automations. This has leveraged the use of some proficient computing paradigms such as multiaccess edge computing (MEC), which facilitates a low latency and scalable solution for the execution of industrial workloads. However, continual generation of industrial data has imposed a substantial amount of stress on the resource-constrained MEC systems. In this perspective, our study proposes a consolidated stochastic computation offloading (CSCO) framework to address the increasing computational demands of MEC-based IIoT systems. The proposed framework efficiently handles industrial workloads by modeling them as stochastic processes to observe the number of data packets denied service due to the finite number of busy MEC servers. We provide an analytical solution corresponding to the loss probability of data packets denied service at the MEC servers. This leads to the development of a computation offloading mechanism for time-critical tasks. Furthermore, we provide the expression for conditional waiting time (CWT) and unconditional waiting time (UWT) of the data packets waiting to be offloaded to the remote cloud servers. Through extensive numerical simulations, it is inferred that the proposed CSCO framework provides promising results in characterizing the stochastic behavior of MEC-based IIoT systems, thereby providing a low latency, and resource sensitive solution for the considered system.