scholarly journals An Efficient Computation Offloading Strategy with Mobile Edge Computing for IoT

Micromachines ◽  
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.

scholarly journals A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Security modeling and efficient computation offloading for service workflow in mobile edge computing

2019 ◽  
Vol 97 ◽  
pp. 755-774 ◽  
Author(s):  
Binbin Huang ◽  
Zhongjin Li ◽  
Peng Tang ◽  
Shangguang Wang ◽  
Jun Zhao ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Security Modeling

scholarly journals Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1010 ◽  
Author(s):  
Prince Waqas Khan ◽  
Khizar Abbas ◽  
Hadil Shaiba ◽  
Ammar Muthanna ◽  
Abdelrahman Abuarqoub ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Mobile Devices ◽  
Energy Efficient ◽  
Linear Optimization ◽  
Edge Computing ◽  
Computation Offloading ◽  
Optimization Approach ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Integer Linear Optimization

Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

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

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Siling Feng ◽  
Yinjie Chen ◽  
Qianhao Zhai ◽  
Mengxing Huang ◽  
Feng Shu
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Grey Wolf Optimizer ◽  
Mobile Edge Computing ◽  
Grey Wolf ◽  
Swarm Optimization ◽  
Whale Optimization ◽  
Two Factors ◽  
Improved Model ◽  
The Internet Of Things

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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