scholarly journals Dynamic Game-Based Computation Offloading and Resource Allocation in LEO-Multiaccess Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Haoyu Wang ◽  
Hengli Wang ◽  
Jianwei An
Keyword(s):  
Nash Equilibrium ◽  
Network Architecture ◽  
Equilibrium Solution ◽  
Dynamic Game ◽  
Open Loop ◽  
Low Earth Orbit ◽  
Edge Computing ◽  
Computation Offloading ◽  
The Relationship ◽  
Task Offloading

The offloading of computing tasks in edge computing has always been a research hotspot and difficulty in recent years. As an effective way to run various applications on mobile devices with limited resources, it has been extensively studied by scholars from all walks of life. However, the traditional ground-based network-based edge computing network architecture cannot meet the needs of edge users with limited geographic areas. Therefore, this paper proposes an LEO (low earth orbit) satellite-based multiaccess edge computing network architecture and establishes a differential game model for this architecture. To obtain the Nash equilibrium solution of the open loop and the Nash equilibrium solution of the feedback for the task offloading amount, the relationship between the user’s income and the QoE level under the optimal task offloading amount is finally analyzed and discussed.

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.

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.

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 Cross-Server Computation Offloading for Multi-Task Mobile Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 96 ◽  
Author(s):  
Yongpeng Shi ◽  
Yujie Xia ◽  
Ya Gao
Keyword(s):  
Approximation Algorithm ◽  
Mobile Devices ◽  
Computational Efficiency ◽  
Network Architecture ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Optimal Solutions ◽  
Resource Constrained ◽  
And Storage

As an emerging network architecture and technology, mobile edge computing (MEC) can alleviate the tension between the computation-intensive applications and the resource-constrained mobile devices. However, most available studies on computation offloading in MEC assume that the edge severs host various applications and can cope with all kinds of computation tasks, ignoring limited computing resources and storage capacities of the MEC architecture. To make full use of the available resources deployed on the edge servers, in this paper, we study the cross-server computation offloading problem to realize the collaboration among multiple edge servers for multi-task mobile edge computing, and propose a greedy approximation algorithm as our solution to minimize the overall consumed energy. Numerical results validate that our proposed method can not only give near-optimal solutions with much higher computational efficiency, but also scale well with the growing number of mobile devices and tasks.

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.

scholarly journals Stackelberg Dynamic Game-Based Resource Allocation in Threat Defense for Internet of Things

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4074 ◽  
Author(s):  
Bingjie Liu ◽  
Haitao Xu ◽  
Xianwei Zhou
Keyword(s):  
Resource Allocation ◽  
Nash Equilibrium ◽  
Internet Of Things ◽  
Rapid Development ◽  
Dynamic Game ◽  
Open Loop ◽  
Game Model ◽  
Equilibrium Solutions ◽  
Iot Devices ◽  
Dynamic Game Model

With the rapid development of the Internet of Things, there are a series of security problems faced by the IoT devices. As the IoT devices are generally devices with limited resources, how to effectively allocate the restricted resources facing the security problems is the key issue at present. In this paper, we study the resource allocation problem in threat defense for the resource-constrained IoT system, and propose a Stackelberg dynamic game model to get the optimal allocated resources for both the defender and attackers. The proposed Stackelberg dynamic game model is composed by one defender and many attackers. Given the objective functions of the defender and attackers, we analyze both the open-loop Nash equilibrium and feedback Nash equilibrium for the defender and attackers. Then both the defender and attackers can control their available resources based on the Nash equilibrium solutions of the dynamic game. Numerical simulation results show that correctness and effeteness of the proposed model.

Binding of lead and copper(II) cations to hemicelluloses of rye bran

1986 ◽  
Vol 51 (10) ◽  
pp. 2250-2258 ◽  
Author(s):  
Rudolf Kohn ◽  
Zdena Hromádková ◽  
Anna Ebringerová
Keyword(s):  
Uronic Acid ◽  
Equilibrium Solution ◽  
Glucuronic Acid ◽  
The Other ◽  
Carboxyl Groups ◽  
Stoichiometric Ratio ◽  
Molar Ratios ◽  
Free Ions ◽  
Uronic Acid Content ◽  
The Relationship

Several fractions of acid hemicelluloses isolated from rye bran were characterized by molar ratios of saccharides (D-Xyl, L-Ara, D-Glc, D-Gal) and 4-O-methyl-D-glucuronic acid and protein content. Binding of Pb2+ and Cu2+ ions to these acid polysaccharides was considered according to function (M)b = f([M2+]f), expressing the relationship between the amount of metal (M)b bound to 1 g of the substance and the concentration of free ions [M2+]f in the equilibrium solution and according to the association degree β of these cations with carboxyl groups of uronic acid at a stoichiometric ratio of both components in the system under investigation. Acid hemicelluloses contained only a very small portion of uronic acid ((COOH) 0.05-0.18 mmol g-1); the model polysaccharide, 4-O-methyl-D-glucurono-D-xylan of beech, was substantially richer in uronic acid content ((COOH) 0.73 mmol g-1). Consequently, the amount of lead and copper bound to acid hemicelluloses is very small ((M)b 0.017-0.025 mmol g-1) at [M2+]f = 0.10 mmol l-1. On the other hand, much greater amount of cations ((M)f 0.09-0.10 mmol g-1) was bound to the glucuronoxylan. The association degree β was like with the majority of samples (β = 0.31-0.38). The amount of lead and copper(II) bound to acid hemicelluloses from rye bran is several times lower than that bound to dietary fiber isolated from vegetables (cabbage, carrot), rich in pectic substances.

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
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