scholarly journals Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83 ◽  
Author(s):  
Zhi Li ◽  
Qi Zhu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Completion Time ◽  
Access Network ◽  
Optimization Method ◽  
Edge Computing ◽  
Smart Devices ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Task Completion Time

Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

scholarly journals Task Scheduling Allocation based on Task Completion Time in Cloud Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 5207-5213
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Task Allocation ◽  
Completion Time ◽  
Resource Provisioning ◽  
Task Completion ◽  
Task Completion Time ◽  
Survey Paper

Cloud computing is a prominent computing model wherein shared resources can be given as per the customer request at a time. The available resources in the cloud are gathered to execute several tasks that are submitted by the customer. While implementing the tasks, there is a need to optimize performance in terms of execution time, response time and resource utilization of the cloud. The optimization of the mentioned factors in the Cloud Computing can be achieved by one of the major areas known as Load balancing which refers to dealing with client requests from diverse application servers that are functioning in the cloud. An efficient Load Balancing algorithm enables the cloud to be more proficient and enhances customer contentment. So, this survey paper highlights the latest studies regarding the application of Load Balancing techniques for task allocation such as resource allocation (RA) strategies, cloud task scheduling centered on Load Balancing, dynamic Resource Allocation schemes, and cloud resource provisioning scheduling heuristics. Finally, Load Balancing performance for task allocation methods is compared based on task completion time.

scholarly journals Mobile edge computing resource allocation: A joint Stackelberg game and matching strategy

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986155 ◽  
Author(s):  
Shaoyong Guo ◽  
Xing Hu ◽  
Gangsong Dong ◽  
Wencui Li ◽  
Xuesong Qiu
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Smart Home ◽  
Stackelberg Game ◽  
Edge Computing ◽  
Hierarchical Architecture ◽  
Smart Devices ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Computing Resource

Mobile edge computing has attracted great interests in the popularity of fifth-generation (5G) networks and Internet of Things. It aims to supply low-latency and high-interaction services for delay-sensitive applications. Utilizing mobile edge computing with Smart Home, which is one of the most important fields of Internet of Things, is a method to satisfy users’ demand for higher computing power and storage capacity. However, due to limited computing resource, how to improve efficiency of resource allocation is a challenge. In this article, we propose a hierarchical architecture in Smart Home with mobile edge computing, providing low-latency services and promoting edge process for smart devices. Based on that, a Stackelberg Game is designed in order to allocate computing resource to devices efficiently. Then, one-to-many matching is established to handle resource allocation problems. It is proved that the allocation strategy can optimize the utility of mobile edge computing server and improve allocating efficiency. Simulation results show the effectiveness of the proposed strategy compared with schemes based on auction game, and present performance with different changing system parameters.

scholarly journals Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Wang
Keyword(s):  
Genetic Algorithm ◽  
Base Station ◽  
Edge Computing ◽  
Base Stations ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Improved Genetic Algorithm ◽  
Collaborative Task ◽  
Good Signal ◽  
Task Offloading

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

2018 ◽  
Vol 15 (2) ◽  
pp. 627-636 ◽  
Author(s):  
K. Padmanabhan Panchu ◽  
M. Rajmohan ◽  
M. R. Sumalatha ◽  
R. Baskaran
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Completion Time ◽  
Route Planning ◽  
Task Completion ◽  
Task Completion Time ◽  
Robot Teams ◽  
Reconfigurable Robot ◽  
Robot Tasks ◽  
Multi Robot

This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2426-2429 ◽  
Author(s):  
Jun Wei Ge ◽  
Yong Sheng Yuan
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Task Completion Time ◽  
Task Scheduling Algorithm ◽  
Reasonable Use

Use genetic algorithm for task allocation and scheduling has get more and more scholars' attention. How to reasonable use of computing resources make the total and average time of complete the task shorter and cost smaller is an important issue. The paper presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Experimental results show that this algorithm is a more effective task scheduling algorithm in the cloud computing environment.

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