Distributed task offloading strategy to low load base stations in mobile edge computing environment

With the development of the mobile Internet, smart mobile terminals have become an indispensable tool for people's lives and mobile applications are becoming more and more powerful. This research mainly discusses the dynamic resource allocation strategy of the mobile edge cloud computing environment. The physical resource layer in the network model is responsible for providing specific resources that are actually available, such as hardware resources, computing resources, storage resources, mainly including base stations, mobile edge computing servers, spectrum, power, and other communications of different infrastructure vendor basic components of the system. The functions of the virtual machine monitor include resource virtualization and resource management. As an important component of wireless network virtualization, virtual machine monitors are usually deployed in physical base stations to provide physical resources and to consider the connection between the virtual machine stations. The business of the business cache model is an application that is requested by users running on the mobile edge computing server or cloud at the base station. The computing task scheduling in the mobile edge environment can be classified as a wireless interaction model. This model captures the user throughput in cellular network interaction. The physical layer channel access strategy (CDMA) allows all mobile users to efficiently share the same spectrum resources at the same time. When the preference coefficient for task energy consumption varies between 0.35–0.55 and 0.65–1, the superior range of maximum system efficiency achieved by RAOM accounts for 55% of the entire range. This research contributes to the reasonable allocation of resources, and the mobile edge computing model improves the fairness of users with a lower transmission cost.

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Optimization Strategy of Task Offloading with Wireless and Computing Resource Management in Mobile Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2021/8288836 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Xintao Wu ◽

Jie Gan ◽

Shiyong Chen ◽

Xu Zhao ◽

Yucheng Wu

Keyword(s):

Resource Management ◽

Optimal Solution ◽

Edge Computing ◽

Base Stations ◽

Mobile Edge Computing ◽

Optimization Strategy ◽

Resource Allocation Algorithm ◽

Computing Resource ◽

Task Offloading

Mobile edge computing (MEC) provides user equipment (UE) with computing capability through wireless networks to improve the quality of experience (QoE). The scenario with multiple base stations and multiple mobile users is modeled and analyzed. The optimization strategy of task offloading with wireless and computing resource management (TOWCRM) in mobile edge computing is considered. A resource allocation algorithm based on an improved graph coloring method is used to allocate wireless resource blocks (RBs). The optimal solution of computing resource is obtained by using KKT conditions. To improve the system utility, a semi-distributed TOWCRM strategy is proposed to obtain the task offloading decision. Theoretical simulations under different system parameters are executed, and the proposed semi-distributed TOWCRM strategy can be completed with finite iterations. Simulation results have verified the effectiveness of the proposed algorithm.

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Computing Resource Allocation Scheme of IOV using Deep Reinforcement Learning in Edge Computing Environment

10.21203/rs.3.rs-335606/v1 ◽

2021 ◽

Author(s):

Yiwei Zhang ◽

Min Zhang ◽

Caixia Fan ◽

Fuqiang Li ◽

Baofang Li

Keyword(s):

Resource Allocation ◽

Reinforcement Learning ◽

Edge Computing ◽

Mobile Edge Computing ◽

Allocation Scheme ◽

Computing Environment ◽

Learning Network ◽

Resource Allocation Scheme ◽

The Mathematical Model ◽

Task Offloading

Abstract With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment.

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

Journal of Robotics ◽

10.1155/2021/3965689 ◽

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.

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