A Computational Offloading Method for Edge Server Computing and Resource Allocation Management

Muna Al-Razgan,Taha Alfakih,Mohammad Mehedi Hassan,Naeem Jan

doi:10.1155/2021/3557059

Abstract

The emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices. We can achieve that by offloading resource intensive applications to remote cloud-based data centers. For the remote computing solution, mobile devices (MDs) experience higher response time and delay of the network, which negatively affects the real-time mobile user applications. In this study, we proposed a model to evaluate the efficiency of the close-end network computation offloading in MEC. This model helps in choosing the adjacent edge server from the surrounding edge servers. This helps to minimize the latency and increase the response time. To do so, we use a decision rule based Heuristic Virtual Value (HVV). The HVV is a mapping function based on the features of the edge server like the workload and performance. Furthermore, we propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. The results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization.

Highlights

Academic Editor: Naeem Jan e emerging technology of mobile cloud is introduced to overcome the constraints of mobile devices
We proposed a model to evaluate the efficiency of the close-end network computation offloading in mobile edge computing (MEC). is model helps in choosing the adjacent edge server from the surrounding edge servers. is helps to minimize the latency and increase the response time
We propose availability of a virtual machine resource algorithm (AVM) based on the availability of VM in edge cloud servers for efficient resource allocation and task scheduling. e results of experiment simulation show that the proposed model can meet the response time requirements of different real-time services, improve the performance, and minimize the consumption of mobile devices (MDs) energy and the resource utilization

Summary

Related Work

In [8], the performance ability or capability is enhanced to leverage the available computing of edge servers and capacity, by the proposing a system which provides a collection of colocated devices as cloud service at the edge and enables leveraging multiple clients into a coordinated cloud computing service despite churn in participation of mobile devices. A novel algorithm of computation is proposed; it depends on the estimated RTT connected with other parameters (e.g., consumption of power) to take a decision as to when to offload application computation tasks of mobile device to the mobile edge computing server [17]. E proposed model automatically selects the computing source based on the following parameters: performance, signal strength, radio bandwidth, and workload We relied on these criteria to choose the suitable edge server. Where ec indicates the power required for communication between mobile device and server edge over the network, ew refers to the power required to wait for the result, ci is the size of task that needs to be offloaded, and b is the bandwidth. If the client fails to communicate with the alternative server, they have to communicate with the remote cloud

Resource Allocation Management in the MEC Based on VM Resource Availability

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