Multi-Objective Optimized Immune Algorithm For Computing Offloading Problem In Edge Computing Scenes of Internet of Vehicles

Abstract With the development of 5G technology, the Internet of Vehicles (IoV) has also received worldwide attention. IoV edge computing achieves the goal of low latency by offloading tasks to the Mobile Edge Computing Server (MECS). However, it is still a challenge to reduce the computing delay of mobile terminal devices while ensuring the low energy consumption and load balancing of servers. In order to solve this problem, the system model, delay model, load balancing model, energy consumption model and objective optimization model are established in this paper. A computational unloading scheme based on multi-objective immune optimization algorithm is proposed. Finally, this scheme is compared with the reference scheme and the literature scheme. Simulation experiments show that the proposed scheme can effectively reduce the average unload delay of users, optimize the workload between servers, and effectively reduce energy consumption. Its performance is better than the schemes in the literature.

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Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽

10.3390/electronics10020190 ◽

2021 ◽

Vol 10 (2) ◽

pp. 190

Author(s):

Wu Ouyang ◽

Zhigang Chen ◽

Jia Wu ◽

Genghua Yu ◽

Heng Zhang

Keyword(s):

Energy Consumption ◽

Load Balancing ◽

Markov Decision Process ◽

Decision Process ◽

Base Station ◽

Edge Computing ◽

Mobile Edge Computing ◽

Task Migration ◽

Migration Strategy ◽

Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

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Multi-objective computation offloading for Internet of Vehicles in cloud-edge computing

Wireless Networks ◽

10.1007/s11276-019-02127-y ◽

2019 ◽

Vol 26 (3) ◽

pp. 1611-1629 ◽

Cited By ~ 6

Author(s):

Xiaolong Xu ◽

Renhao Gu ◽

Fei Dai ◽

Lianyong Qi ◽

Shaohua Wan

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Internet Of Vehicles ◽

Multi Objective

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Probabilistic Dominance-Based Multi-Objective Immune Optimization Algorithm in Noisy Environments

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2007.2428 ◽

2007 ◽

Vol 4 (7) ◽

pp. 1380-1387 ◽

Cited By ~ 1

Author(s):

Zhuhong Zhang ◽

Xin Tu

Keyword(s):

Optimization Algorithm ◽

Noisy Environments ◽

Immune Optimization ◽

Multi Objective ◽

Immune Optimization Algorithm

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Multi-objective optimization of container-based microservice scheduling in edge computing

Computer Science and Information Systems ◽

10.2298/csis200229041f ◽

2020 ◽

pp. 41-41

Author(s):

Guisheng Fan ◽

Liang Chen ◽

Huiqun Yu ◽

Wei Qi

Keyword(s):

Load Balancing ◽

Fitness Function ◽

Rapid Development ◽

Edge Computing ◽

Multi Objective Optimization ◽

Simulation Experiments ◽

Multi Objective ◽

Effectiveness And Efficiency ◽

Container Orchestration ◽

Microservice Architecture

Edge computing provides physical resources closer to end users, becoming a good complement to cloud computing.With the rapid development of container technology and microservice architecture, container orchestration has become a hot issue. However, the container-based microservice scheduling problem in edge computing is still urgent to be solved. In this paper, we first formulate the containerbased microservice scheduling as a multi-objective optimization problem, aiming to optimize network latency among microservices, reliability of microservice applications and load balancing of the cluster. We further propose a latency, reliability and load balancing aware scheduling (LRLBAS) algorithm to determine the container-based microservice deployment in edge computing. Our proposed algorithm is based on particle swarm optimization (PSO). In addition, we give a handling strategy to separate the fitness function from constraints, so that each particle has two fitness values. In the proposed algorithm, a new particle comparison criterion is introduced and a certain proportion of infeasible particles are reserved adaptively. Extensive simulation experiments are conducted to demonstrate the effectiveness and efficiency of the proposed algorithm compared with other related algorithms.

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