scholarly journals Markov Approximation for Task Offloading and Computation Scaling in Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Wenchen Zhou ◽  
Weiwei Fang ◽  
Yangyang Li ◽  
Bo Yuan ◽  
Yiming Li ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Task Assignment ◽  
Edge Computing ◽  
Computation Offloading ◽  
Approximation Technique ◽  
Markov Approximation ◽  
Mobile Edge Computing ◽  
Computing Services ◽  
Cloud Computing Services ◽  
Task Offloading

Mobile edge computing (MEC) provides cloud-computing services for mobile devices to offload intensive computation tasks to the physically proximal MEC servers. In this paper, we consider a multiserver system where a single mobile device asks for computation offloading to multiple nearby servers. We formulate this offloading problem as the joint optimization of computation task assignment and CPU frequency scaling, in order to minimize a tradeoff between task execution time and mobile energy consumption. The resulting optimization problem is combinatorial in essence, and the optimal solution generally can only be obtained by exhaustive search with extremely high complexity. Leveraging the Markov approximation technique, we propose a light-weight algorithm that can provably converge to a bounded near-optimal solution. The simulation results show that the proposed algorithm is able to generate near-optimal solutions and outperform other benchmark algorithms.

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

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.

scholarly journals Task Offloading and Scheduling Strategy for Intelligent Prosthesis in Mobile Edge Computing Environment

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Ping Qi
Keyword(s):  
Deep Learning ◽  
Energy Consumption ◽  
Learning Algorithm ◽  
Problem Definition ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Intent Recognition ◽  
Scheduling Strategy ◽  
Task Offloading

Traditional intent recognition algorithms of intelligent prosthesis often use deep learning technology. However, deep learning’s high accuracy comes at the expense of high computational and energy consumption requirements. Mobile edge computing is a viable solution to meet the high computation and real-time execution requirements of deep learning algorithm on mobile device. In this paper, we consider the computation offloading problem of multiple heterogeneous edge servers in intelligent prosthesis scenario. Firstly, we present the problem definition and the detail design of MEC-based task offloading model for deep neural network. Then, considering the mobility of amputees, the mobility-aware energy consumption model and latency model are proposed. By deploying the deep learning-based motion intent recognition algorithm on intelligent prosthesis in a real-world MEC environment, the effectiveness of the task offloading and scheduling strategy is demonstrated. The experimental results show that the proposed algorithms can always find the optimal task offloading and scheduling decision.

scholarly journals Computation efficiency optimization in UAV-enabled mobile edge computing system with multi-carrier non-orthogonal multiple access

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Fangcheng Xu ◽  
Xiangbin Yu ◽  
Jiali Cai ◽  
Guangying Wang
Keyword(s):  
Multiple Access ◽  
Initial Point ◽  
Optimal Solution ◽  
Arbitrary Point ◽  
Polynomial Time Algorithm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Computation Efficiency

Abstract In this paper, we study the issue of fair resource optimization for an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multi-carrier non-orthogonal multiple access (MC-NOMA). A computation efficiency (CE) optimization problem based on the max-min fairness principle under the partial offloading mode is formulated by optimizing the subchannel assignment, the local CPU frequency, and the transmission power jointly. The formulated problem belongs to the non-convex mixed integer nonlinear programming (MINLP), that is NP-hard to find the global optimal solution. Therefore, we design a polynomial-time algorithm based on the big-M reformulation, the penalized sequential convex programming, and the general Dinkelbach’s method, which can choose an arbitrary point as the initial point and eventually converge to a feasible suboptimal solution. The proposed algorithm framework can be also applied to computation offloading only mode. Additionally, we derive the closed-form optimal solution under the local computing only mode. Simulation results validate the convergence performance of the proposed algorithm. Moreover, the proposed partial offloading mode with the CE maximization scheme outperforms that with the computation bits (CB) maximization scheme with respect to CE, and it can achieve higher CE than the benchmark computing modes. Furthermore, the proposed MC-NOMA scheme can attain better CE performance than the conventional OFDMA scheme.

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

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.

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

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.

scholarly journals Distributed Task Offloading Game in Multiserver Mobile Edge Computing Networks

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Shuang Chen ◽  
Ying Chen ◽  
Xin Chen ◽  
Yuemei Hu
Keyword(s):  
Lower Cost ◽  
Edge Computing ◽  
Computation Offloading ◽  
Noncooperative Game ◽  
Mobile Edge Computing ◽  
Data Traffic ◽  
Service Areas ◽  
Two Stages ◽  
Time Update ◽  
Task Offloading

With the explosion of data traffic, mobile edge computing (MEC) has emerged to solve the problem of high time delay and energy consumption. In order to cope with a large number of computing tasks, the deployment of edge servers is increasingly intensive. Thus, server service areas overlap. We focus on mobile users in overlapping service areas and study the problem of computation offloading for these users. In this paper, we consider a multiuser offloading scenario with intensive deployment of edge servers. In addition, we divide the offloading process into two stages, namely, data transmission and computation execution, in which channel interference and resource preemption are considered, respectively. We apply the noncooperative game method to model and prove the existence of Nash equilibrium (NE). The real-time update computation offloading algorithm (RUCO) is proposed to obtain equilibrium offloading strategies. Due to the high complexity of the RUCO algorithm, the multiuser probabilistic offloading decision (MPOD) algorithm is proposed to improve this problem. We evaluate the performance of the MPOD algorithm through experiments. The experimental results show that the MPOD algorithm can converge after a limited number of iterations and can obtain the offloading strategy with lower cost.

Computation Offloading Game in Multiple UAV-Enabled Mobile Edge Computing Networks

2020 ◽  
Author(s):  
Yanling Ren ◽  
Zhibin Xie ◽  
Zhenfeng Ding ◽  
xiyuan sun ◽  
Jie Xia ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing

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.

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