An efficient latency aware resource provisioning in cloud assisted mobile edge framework

Abstract

Mobile edge computing is developing as an innovative computing paradigm that gives improved practice to mobile users through low latency connections and enlarged computation limits. As the amount of user requests is time- different, while the computation limit of the edge has is constrained, the Cloud Assisted Mobile Edge computing system is acquainted with improving the adaptability of the edge platform. To give ensured administrations at negligible framework latency, the edge resource provisioning and cloud redistributing of the cloud-assisted mobile edge computing structure ought to be wisely planned effectively. This work proposed a latency aware resource provisioning strategy for distributed cloud-assisted mobile edge computing structure. At first, the framework gets SFC requests for Virtual network functions (VNFs) to use both edge and cloud assets. Here, the efficient parameters, for example, execution time and workload of VNFs are evaluated and Fuzzy logic-based auto-scaling is executed for the overloaded VNFs that need more assets because of the progressively expanded measure of the system packets. Subsequently, the SFC requests are scheduled to the cloud-assisted edge network adequately utilizing the Adaptive Grey Wolf Optimization (AGWO) based asset provisioning algorithm. The exploratory outcomes show the superiority of the presented methodology comparing with the existing techniques as far as system cost, arrival rate, and average response time.