Multi-Objective Whale Optimization Algorithm for Computation Offloading Optimization in Mobile Edge Computing

Abstract

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.

A Novel Self-Adaptive Mixed-Variable Multiobjective Ant Colony Optimization Algorithm in Mobile Edge Computing

“…where ω j can be calculated by using various formulas. In this paper, the Gaussian function g(μ, σ) � g(1, qk) is selected, which formula is as (2). Besides, q is the algorithm parameter, and k is the number of solutions in the solution archive.…”

“…where ω jl is the weight corresponding to the best quality solution in the solution archive whose value of the dimension variable is not empty, and it can calculate as (2). In particular, if this dimension variable of all solutions is empty, then ω jl is taken as 0. u i l is the number of solutions whose value of this dimension variable is not empty in the solution archive.…”

“…en the C D(S x ) is d(x, T) multiplied by the adjustment coefficient α; the calculation process is as equation (2).…”

“…Liu et al [1] propose a multiobjective resource allocation method, named MRAM, and the method is leveraged to optimize the time cost of IoT applications, load balance, and energy consumption of MEC servers. Huang et al [2] present a multiobjective whale optimization algorithm (MOWOA) based on time and energy consumption to solve the optimal offloading mechanism of computation offloading in MEC. Fan et al [3] propose an algorithm based on particle swarm optimization (PSO) to solve the MOO of the container-based microservice scheduling, aiming to optimize network latency among microservices, reliability of microservice applications, and load balancing of the cluster.…”

HAGP: A Heuristic Algorithm Based on Greedy Policy for Task Offloading with Reliability of MDs in MEC of the Industrial Internet

“…However, task offloading decisions in MEC are easily affected by many uncertain factors, such as unstable mobile wireless channels, resulting in unpredictable latency and more energy consumption caused by unnecessary task re-transmission, which may seriously degrade the system performance [ 12 , 13 ]. Moreover, complex task offloading schemes or mechanisms will also consume precious resources, e.g., battery power in MDs, which will further influence the system performance [ 14 , 15 , 16 ]. Therefore, ensuring reliable task offloading in MEC is a must and necessary requirement in realistic Industrial Internet application scenarios.…”