Optimizing computation offloading strategy in mobile edge computing based on swarm intelligence algorithms

AbstractAs the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA.

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

Sensors ◽

10.3390/s21082628 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2628

Author(s):

Mengxing Huang ◽

Qianhao Zhai ◽

Yinjie Chen ◽

Siling Feng ◽

Feng Shu

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Solution Set ◽

Whale Optimization Algorithm ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Multi Objective ◽

Whale Optimization ◽

Artificial Immune System Algorithm

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.

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Computation Offloading Strategy for Improved Particle Swarm Optimization in Mobile Edge Computing

2021 IEEE 6th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) ◽

10.1109/icccbda51879.2021.9442609 ◽

2021 ◽

Author(s):

Shun Li ◽

Haibo Ge ◽

Xutao Chen ◽

Linhuan Liu ◽

Haiwen Gong ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Swarm Optimization ◽

Improved Particle Swarm Optimization

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Particle Swarm Optimization-Based Secure Computation Efficiency Maximization in a Power Beacon-Assisted Wireless-Powered Mobile Edge Computing NOMA System

Energies ◽

10.3390/en13215540 ◽

2020 ◽

Vol 13 (21) ◽

pp. 5540 ◽

Cited By ~ 1

Author(s):

Carla E. Garcia ◽

Mario R. Camana ◽

Insoo Koo

Keyword(s):

Particle Swarm Optimization ◽

Multiple Access ◽

Particle Swarm ◽

Secure Computation ◽

Edge Computing ◽

Computation Offloading ◽

Processing Unit ◽

Mobile Edge Computing ◽

Swarm Optimization ◽

Computation Efficiency

In this paper, we aim to provide reliable user connectivity and enhanced security for computation task offloading. Physical layer security is studied in a wireless-powered non-orthogonal multiple access (NOMA) mobile edge computing (MEC) system with a nonlinear energy-harvesting (EH) user and a power beacon (PB) in the presence of an eavesdropper. To further provide a friendly environment resource allocation design, wireless power transfer (WPT) is applied. The secure computation efficiency (SCE) problem is solved by jointly optimizing the transmission power, the time allocations for energy transfer, the computation time, and the central processing unit (CPU) frequency in the NOMA-enabled MEC system. The problem is non-convex and challenging to solve because of the complexity of the objective function in meeting constraints that ensure the required quality of service, such as the minimum value of computed bits, limitations on total energy consumed by users, maximum CPU frequency, and minimum harvested energy and computation offloading times. Therefore, in this paper, a low-complexity particle swarm optimization (PSO)-based algorithm is proposed to solve this optimization problem. For comparison purposes, time division multiple access and fully offloading baseline schemes are investigated. Finally, simulation results demonstrate the superiority of the proposed approach over baseline schemes.

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Research on Task-Offloading Decision Mechanism in Mobile Edge Computing based Internet of Vehicle

10.21203/rs.3.rs-225233/v1 ◽

2021 ◽

Author(s):

Jun Cheng ◽

Dejun Guan

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Cloud Infrastructure ◽

Swarm Optimization ◽

Decision Mechanism ◽

The Impact ◽

Task Offloading

Abstract As a technology integrated with Internet of Things (IoT), mobile edge computing (MEC) can provide real-time and low latency services to the underlying network, and improve the storage and computation ability of the networks instead of central cloud infrastructure. In Mobile Edge Computing based Internet of Vehicle(MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers Based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for IoV-based edge computing is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization (PSO) is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

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Computation Offloading Based on Improved Glowworm Swarm Optimization Algorithm in Mobile Edge Computing

Journal of Physics Conference Series ◽

10.1088/1742-6596/1757/1/012195 ◽

2021 ◽

Vol 1757 (1) ◽

pp. 012195

Author(s):

Ke Fu ◽

Jun Ye

Keyword(s):

Optimization Algorithm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Swarm Optimization ◽

Glowworm Swarm Optimization

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Energy-optimized Partial Computation Offloading in Mobile Edge Computing with Genetic Simulated-annealing-based Particle Swarm Optimization

IEEE Internet of Things Journal ◽

10.1109/jiot.2020.3024223 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Jing Bi ◽

Haitao Yuan ◽

Shuaifei Duanmu ◽

Meng Chu Zhou ◽

Abdullah Abusorrah

Keyword(s):

Particle Swarm Optimization ◽

Simulated Annealing ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Swarm Optimization

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Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01984-6 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Jun Cheng ◽

Dejun Guan

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Cloud Infrastructure ◽

Swarm Optimization ◽

Decision Mechanism ◽

The Impact ◽

Task Offloading

AbstractAs a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

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An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Micromachines ◽

10.3390/mi12020204 ◽

2021 ◽

Vol 12 (2) ◽

pp. 204

Author(s):

Juan Fang ◽

Jiamei Shi ◽

Shuaibing Lu ◽

Mengyuan Zhang ◽

Zhiyuan Ye

Keyword(s):

Edge Computing ◽

Computation Offloading ◽

Mobile Edge Computing ◽

Efficient Computation ◽

Fine Grained ◽

Delay Sensitive ◽

Starting Point ◽

Computationally Intensive ◽

Execution Delay ◽

The Internet Of Things

With the rapidly development of mobile cloud computing (MCC), the Internet of Things (IoT), and artificial intelligence (AI), user equipment (UEs) are facing explosive growth. In order to effectively solve the problem that UEs may face with insufficient capacity when dealing with computationally intensive and delay sensitive applications, we take Mobile Edge Computing (MEC) of the IoT as the starting point and study the computation offloading strategy of UEs. First, we model the application generated by UEs as a directed acyclic graph (DAG) to achieve fine-grained task offloading scheduling, which makes the parallel processing of tasks possible and speeds up the execution efficiency. Then, we propose a multi-population cooperative elite algorithm (MCE-GA) based on the standard genetic algorithm, which can solve the offloading problem for tasks with dependency in MEC to minimize the execution delay and energy consumption of applications. Experimental results show that MCE-GA has better performance compared to the baseline algorithms. To be specific, the overhead reduction by MCE-GA can be up to 72.4%, 38.6%, and 19.3%, respectively, which proves the effectiveness and reliability of MCE-GA.

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