Joint Network Selection and Service Placement Based on Particle Swarm Optimization for Multi-Access Edge Computing

Abstract

With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This paper studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC's access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this paper designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this paper designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost. INDEX TERMS Multi-access edge computing, particle swarm optimization, quality-of-service.

Multi-Objective Accelerated Particle Swarm Optimization With Dynamic Programing Technique for Resource Allocation in Mobile Edge Computing

“…The authors described MEC network selection as an NP-hard problem and proposed a PSO-based algorithm as a solution. The algorithm focused on service placement and network selection by mapping each task to the appropriate edge [30]. It consisted of a three-tier architecture containing a centralized cloud, roadside cloudlet, and vehicular cloud.…”

“…In the research of [31], the authors proposed a hybrid adaptive PSO (HAPSO) algorithm as an optimization process for resource allocation. The authors focused on three objectives (i.e., a MOO problem): namely, to enhance network latency, reduce total energy consumption, and increase availability [30]. They examined the migration strategy in the MEC to transfer services from the early nodes to other edge nodes that can offer services to meet QoS by resource allocation to reduce time service and energy consumption.…”

AI based Service Management for 6G Green Communications

“…2) Computation Resource Allocation: The computation platform usually needs to execute multiple tasks. How to allocate the computation resource, especially the CPU/GPU cycles is an attractive topic [87], [109], [113], [247]. On the other hand, energy consumption is also an important metric that needs to be considered.…”

“…Ma et al [247] utilize the PSO algorithm to jointly optimize the selection of access networks and edge cloud to minimize the latency and total energy consumption. In the considered scenario, each user can be served by multiple edge cloudenabled access networks.…”

A convex problem optimization solution for information management in edge computing platforms

“…The optimization techniques define resource management and provide a better evaluation method. The optimization determines the information management and estimates the computing based on the data partitioning (Ma et al, 2020). The data split is done for the resource management in edge computation and derives better optimization solves the convex optimization issue.…”