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A vast array of IoT devices have been implemented worldwide as cloud computing and virtualization have become more and more commonplace. Because of this, processing of computationally demanding tasks is moved from the edge to the centralised cloud, increasing energy consumption in cloud data centres and causing a noticeable delay for vital applications. The paradigm for cloud computing has shifted in recent years from centralised to mobile edge computing (MEC), where computational workloads are delegated to edge servers close to user equipment (UE). This epitome results in minimal latency for UE, effective resource utilisation at the edge, and decreased energy consumption in cloud data centres. Given this, developing optimal energy-saving solutions for resource allocation and computing job offloading in MEC networks has become a challenging challenge due to the rapidly growing size and complexity of these networks. The vacation policy is a suggested power-saving traffic management strategy for edge servers. Software-defined network (SDN) architecture allows virtual machines (VMs) to take vacations. The goal of QoS optimisation is to decrease the active servers serving the tasks while maintaining QoS criteria. Numerous performance metrics of the system are calculated, including average system size, sojourn duration, percentage of client requests fulfilled, and frequency of desertion. Lastly, many numerical results are given to demonstrate the influence of certain particular factors on the MEC performance metrics.
Rout et al. (Fri,) studied this question.