Multi-objective optimization of task assignment in distributed mobile edge computing

2021 ◽  
Author(s):  
Sanaa Almasri ◽  
Moath Jarrah ◽  
Basheer Al-Duwairi
Keyword(s):  
Task Assignment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Joint Task Offloading, Resource Allocation, and Security Assurance for Mobile Edge Computing-Enabled UAV-Assisted VANETs

2021 ◽  
Vol 13 (8) ◽  
pp. 1547
Author(s):  
Yixin He ◽  
Daosen Zhai ◽  
Fanghui Huang ◽  
Dawei Wang ◽  
Xiao Tang ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Optimization Problem ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Objective Optimization ◽  
Dual Decomposition ◽  
Security Assurance ◽  
Multi Objective ◽  
Task Processing ◽  
Task Offloading

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc network (VANET) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on the UAV due to the limited computation ability. To counter the problems above, we first model and analyze the transmission model and the security assurance model from the vehicle to the MEC server on UAV, and the task computation model of the local vehicle and the edge UAV. Then, the vehicle offloading problem is formulated as a multi-objective optimization problem by jointly considering the task offloading, the resource allocation, and the security assurance. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed scheme achieves significant performance superiority compared with other schemes in terms of the successful task processing ratio and the task processing delay.

scholarly journals Multi-Task Scheduling Based on Classification in Mobile Edge Computing

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 938 ◽  
Author(s):  
Xiao Zheng ◽  
Yuanfang Chen ◽  
Muhammad Alam ◽  
Jun Guo
Keyword(s):  
Task Scheduling ◽  
Upper Bound ◽  
Optimization Algorithms ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Edge Computing ◽  
Pareto Optimal ◽  
Mobile Edge Computing ◽  
Multi Objective Optimization ◽  
Multi Objective

In this paper, a dynamic multi-task scheduling prototype is proposed to improve the limited resource utilization in the vehicular networks (VNET) assisted by mobile edge computing (MEC). To ensure quality of service (QoS) and meet the growing data demands, multi-task scheduling strategies should be specially constructed by considering vehicle mobility and hardware service constraints. We investigate the rational scheduling of multiple computing tasks to minimize the VNET loss. To avoid conflicts between tasks when the vehicle moves, we regard multi-task scheduling (MTS) as a multi-objective optimization (MOO) problem, and the whole goal is to find the Pareto optimal solution. Therefore, we develop some gradient-based multi-objective optimization algorithms. Those optimization algorithms are unable to deal with large-scale task scheduling because they become unscalable as the task number and gradient dimensions increase. We therefore further investigate an upper bound of the loss of multi-objective and prove that it can be optimized in an effective way. Moreover, we also reach the conclusion that, with practical assumptions, we can produce a Pareto optimal solution by upper bound optimization. Compared with the existing methods, the experimental results show that the accuracy is significantly improved.

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

Sensors ◽  
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.

Task assignment in mobile edge computing networks: a deep reinforcement learning approach

2021 ◽  
Author(s):  
Mingjie Feng ◽  
Qi Zhao ◽  
Nichole Sullivan ◽  
Genshe Chen ◽  
Khanh Pham ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Task Assignment ◽  
Edge Computing ◽  
Learning Approach ◽  
Mobile Edge Computing
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