Research Article A Computational Offloading Method for Edge Server Computing and Resource Allocation Management Muna Al-Razgan, 1 Taha Alfakih , 2 and Mohammad Mehedi Hassan 2 1 Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11345, Saudi Arabia 2 Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11345, Saudi Arabia CorrespondenceshouldbeaddressedtoTahaAlfakih;talfakih@ksu.edu.sa Received 24 October 2021; Accepted 22 November 2021; Published 21 December 2021 AcademicEditor:NaeemJan Copyright © 2021 Muna Al-Razgan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. eemergingtechnologyofmobilecloudisintroducedtoovercometheconstraintsofmobiledevices.Wecanachievethatby offloadingresourceintensiveapplicationstoremotecloud-baseddatacenters.Fortheremotecomputingsolution,mobiledevices (MDs)experiencehigherresponsetimeanddelayofthenetwork,whichnegativelyaffectsthereal-timemobileuserapplications. Inthisstudy,weproposedamodeltoevaluatetheefficiencyoftheclose-endnetworkcomputationoffloadinginMEC.ismodel helpsinchoosingtheadjacentedgeserverfromthesurroundingedgeservers.ishelpstominimizethelatencyandincreasethe responsetime.Todoso,weuseadecisionrulebasedHeuristicVirtualValue(HVV).eHVVisamappingfunctionbasedonthe featuresoftheedgeserverliketheworkloadandperformance.Furthermore,weproposeavailabilityofavirtualmachineresource algorithm(AVM)basedontheavailabilityofVMinedgecloudserversforefficientresourceallocationandtaskscheduling.e resultsofexperimentsimulationshowthattheproposedmodelcanmeettheresponsetimerequirementsofdifferentreal-time services, improve the performance, and minimize the consumption of MD energy and the resource utilization. 1. Introduction ere are more issues facing mobile devices such as smart phones, reconnaissance planes (drones), robots, patient monitoring devices, and wireless sensors, due to limited specifications of these devices (like storage, memory, CPU, battery), and intensive applications (e.g., real-time transla- tion, video processing, image processing) require super- computing. Mobile devices with limited resources are not efficient or opportune to process those applications. erefore, mobile cloud computing (MCC) emerged as a solution to overcome the resource limitation of mobile devices by using computation offloading. Computation offloading is a transfer mechanism of softwareapplicationprocessesfromlimitedresourcedevices toresource-richplatforms.Mobilecloudisthewell-known moduleforMDcomputationoffloading.MCCisbecominga popular technique for mobile services (MS), e.g., video streaming, mobile video games, social networking, educa- tion, mobile healthcare services, and instant messaging [1]. Wirelesscommunicationlimitationslikedisconnection, low bandwidth, security issues, latency, and mobility are considered the key barriers to offloading computation in cloudcomputing(CC)[2].Real-timeservicesareextremely latency sensitive and thus require computing data in close proximity to MDs. erefore, a proximity cloud like MEC, fog computing, and cloudlet can be an efficient and ap- propriate module for computation offloading. Edgecomputing(EC)isanemergingtechnique,whichis currentlybeingstandardizedinETSIIndustrySpecification Group (ISG). EC offers a service of IT environment and capabilities of CC at the mobile network edge, within the Radio Access Network (RAN), and in close proximity to mobile users (MU). e aims are to minimize the services Hindawi Journal of Mathematics Volume 2021, Article ID 3557059, 11 pages https://doi.org/10.1155/2021/3557059