scholarly journals Mobile Edge Computing

2019 ◽  
pp. 29-31
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M. Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Battery Power ◽  
And Storage

Mobile applications are becoming increasingly computational intensive, while many mobile devices still have limited battery power and cannot support computational intensive tasks. Mobile edge computing (MEC) computing is an extension of edge computing, and it refers to computing at the edge of a network. In mobile edge computing, computing and storage nodes are placed at the Internet's edge near mobile devices. It places the edge clouds at the candidate locations. This paper presents a brief introduction to MEC.

scholarly journals Security and Cost-Aware Computation Offloading via Deep Reinforcement Learning in Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Binbin Huang ◽  
Yangyang Li ◽  
Zhongjin Li ◽  
Linxuan Pan ◽  
Shangguang Wang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile User ◽  
Edge Computing ◽  
Computation Offloading ◽  
Security Threats ◽  
Mobile Edge Computing ◽  
Computing Environment ◽  
Markov Decision

With the explosive growth of mobile applications, mobile devices need to be equipped with abundant resources to process massive and complex mobile applications. However, mobile devices are usually resource-constrained due to their physical size. Fortunately, mobile edge computing, which enables mobile devices to offload computation tasks to edge servers with abundant computing resources, can significantly meet the ever-increasing computation demands from mobile applications. Nevertheless, offloading tasks to the edge servers are liable to suffer from external security threats (e.g., snooping and alteration). Aiming at this problem, we propose a security and cost-aware computation offloading (SCACO) strategy for mobile users in mobile edge computing environment, the goal of which is to minimize the overall cost (including mobile device’s energy consumption, processing delay, and task loss probability) under the risk probability constraints. Specifically, we first formulate the computation offloading problem as a Markov decision process (MDP). Then, based on the popular deep reinforcement learning approach, deep Q-network (DQN), the optimal offloading policy for the proposed problem is derived. Finally, extensive experimental results demonstrate that SCACO can achieve the security and cost efficiency for the mobile user in the mobile edge computing environment.

scholarly journals Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

2019 ◽  
Vol 9 (6) ◽  
pp. 4908
Author(s):  
Mohamed El Ghmary ◽  
Tarik Chanyour ◽  
Youssef Hmimz ◽  
Mohammed Ouçamah Cherkaoui Malki
Keyword(s):  
Mobile Devices ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Heuristic Solution ◽  
Huge Impact ◽  
Energy Constrained ◽  
Processing And Storage ◽  
Smart Mobile ◽  
Computational Resources ◽  
And Storage

<span>With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes.</span>

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 A Survey on Mobile Edge Computing: Focusing on Service Adoption and Provision

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Kai Peng ◽  
Victor C. M. Leung ◽  
Xiaolong Xu ◽  
Lixin Zheng ◽  
Jiabin Wang ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Networks ◽  
Edge Computing ◽  
Computation Offloading ◽  
Battery Life ◽  
Mobile Edge Computing ◽  
Service Adoption ◽  
Comprehensive Survey ◽  
And Storage ◽  
Execution Delay

Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). However, this mode may cause significant execution delay. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. In this paper, we present a comprehensive survey of the MEC research from the perspective of service adoption and provision. We first describe the overview of MEC, including the definition, architecture, and service of MEC. After that we review the existing MUs-oriented service adoption of MEC, i.e., offloading. More specifically, the study on offloading is divided into two key taxonomies: computation offloading and data offloading. In addition, each of them is further divided into single MU offloading scheme and multi-MU offloading scheme. Then we survey edge server- (ES-) oriented service provision, including technical indicators, ES placement, and resource allocation. In addition, other issues like applications on MEC and open issues are investigated. Finally, we conclude the paper.

scholarly journals Optimal Offloading Decision Strategies and Their Influence Analysis of Mobile Edge Computing

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3231 ◽  
Author(s):  
Jiuyun Xu ◽  
Zhuangyuan Hao ◽  
Xiaoting Sun
Keyword(s):  
Energy Consumption ◽  
Time Delay ◽  
Branch And Bound ◽  
Edge Computing ◽  
Optimal Decision ◽  
Mobile Edge Computing ◽  
Wireless Devices ◽  
Influence Analysis ◽  
Cloud Servers ◽  
And Storage

Mobile edge computing (MEC) has become more popular both in academia and industry. Currently, with the help of edge servers and cloud servers, it is one of the substantial technologies to overcome the latency between cloud server and wireless device, computation capability and storage shortage of wireless devices. In mobile edge computing, wireless devices take responsibility with input data. At the same time, edge servers and cloud servers take charge of computation and storage. However, until now, how to balance the power consumption of edge devices and time delay has not been well addressed in mobile edge computing. In this paper, we focus on strategies of the task offloading decision and the influence analysis of offloading decisions on different environments. Firstly, we propose a system model considering both energy consumption and time delay and formulate it into an optimization problem. Then, we employ two algorithms—Enumerating and Branch-and-Bound—to get the optimal or near-optimal decision for minimizing the system cost including the time delay and energy consumption. Furthermore, we compare the performance between two algorithms and draw the conclusion that the comprehensive performance of Branch-and-Bound algorithm is better than that of the other. Finally, we analyse the influence factors of optimal offloading decisions and the minimum cost in detail by changing key parameters.

Heterogeneous and Elastic Computation Framework for Mobile Cloud Computing

2014 ◽  
Vol 24 (07) ◽  
pp. 1013-1037 ◽  
Author(s):  
Chi-Sheng Shih ◽  
Joen Chen ◽  
Yu-Hsin Wang ◽  
Norman Chang
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Network Capacity ◽  
Physical Computation ◽  
Imprecise Computation ◽  
Cloud Servers ◽  
Real Time Computing ◽  
And Storage ◽  
Computing Literature

The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.

scholarly journals Privacy-Aware Online Task Offloading for Mobile-Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Dali Zhu ◽  
Ting Li ◽  
Haitao Liu ◽  
Jiyan Sun ◽  
Liru Geng ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Location Privacy ◽  
Optimization Problems ◽  
Energy Performance ◽  
Edge Computing ◽  
System Level ◽  
Mobile Edge Computing ◽  
Task Offloading

Mobile edge computing (MEC) has been envisaged as one of the most promising technologies in the fifth generation (5G) mobile networks. It allows mobile devices to offload their computation-demanding and latency-critical tasks to the resource-rich MEC servers. Accordingly, MEC can significantly improve the latency performance and reduce energy consumption for mobile devices. Nonetheless, privacy leakage may occur during the task offloading process. Most existing works ignored these issues or just investigated the system-level solution for MEC. Privacy-aware and user-level task offloading optimization problems receive much less attention. In order to tackle these challenges, a privacy-preserving and device-managed task offloading scheme is proposed in this paper for MEC. This scheme can achieve near-optimal latency and energy performance while protecting the location privacy and usage pattern privacy of users. Firstly, we formulate the joint optimization problem of task offloading and privacy preservation as a semiparametric contextual multi-armed bandit (MAB) problem, which has a relaxed reward model. Then, we propose a privacy-aware online task offloading (PAOTO) algorithm based on the transformed Thompson sampling (TS) architecture, through which we can (1) receive the best possible delay and energy consumption performance, (2) achieve the goal of preserving privacy, and (3) obtain an online device-managed task offloading policy without requiring any system-level information. Simulation results demonstrate that the proposed scheme outperforms the existing methods in terms of minimizing the system cost and preserving the privacy of users.

scholarly journals Selective Offloading by Exploiting ARIMA-BP for Energy Optimization in Mobile Edge Computing Networks

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 48 ◽  
Author(s):  
Ming Zhao ◽  
Ke Zhou
Keyword(s):  
Cloud Computing ◽  
Energy Consumption ◽  
Mobile Devices ◽  
Energy Optimization ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Innovative Technique ◽  
Simulation Results ◽  
Bp Model

Mobile Edge Computing (MEC) is an innovative technique, which can provide cloud-computing near mobile devices on the edge of networks. Based on the MEC architecture, this paper proposes an ARIMA-BP-based Selective Offloading (ABSO) strategy, which minimizes the energy consumption of mobile devices while meeting the delay requirements. In ABSO, we exploit an ARIMA-BP model for estimating computation capacity of the edge cloud, and then design a Selective Offloading Algorithm for obtaining offloading strategy. Simulation results reveal that the ABSO can apparently decrease the energy consumption of mobile devices in comparison with other offloading methods.

Optimal Offloading for Streaming Applications in Mobile Edge Computing

2021 ◽  
Author(s):  
Pengfei Sun ◽  
Xue-Yang Zhu ◽  
Ya Gao
Keyword(s):  
Mobile Devices ◽  
Rapid Development ◽  
Heuristic Method ◽  
Exact Algorithm ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Computationally Efficient ◽  
Streaming Applications ◽  
Smart Mobile

With the rapid development of smart mobile devices, mobile applications are becoming more and more popular. Since mobile devices usually have constrained computing capacity, computation offloading to mobile edge computing (MEC) to achieve a lower latency is a promising paradigm. In this paper, we focus on the optimal offloading problem for streaming applications in MEC. We present solutions to find offloading policies of streaming applications to achieve an optimal latency. Streaming applications are modeled with synchronous data flow graphs. Two architecture assumptions are considered — with sufficient processors on both the local device and the MEC server, and with a limited number of processors on both sides. The problem is generally NP-complete. We present an exact algorithm and a heuristic algorithm for the former architecture assumption and a heuristic method for the latter. We carry out our experiments on a practical application and thousands of synthetic graphs to comprehensively evaluate our methods. The experimental results show that our methods are effective and computationally efficient.

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