scholarly journals Hierarchical Mobile Edge Computing Architecture Based on Context Awareness

2018 ◽  
Vol 8 (7) ◽  
pp. 1160 ◽  
Author(s):  
Juyong Lee ◽  
Jihoon Lee
Keyword(s):  
Mobile Network ◽  
Content Delivery ◽  
Edge Computing ◽  
Multimedia Streaming ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Computing Services ◽  
Recent Developments ◽  
Computing Performance ◽  
Fast Access

Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

Context-aware and Adaptive QoS Prediction for Mobile Edge Computing Services

2020 ◽  
pp. 1-1
Author(s):  
Zhi-Zhong Liu ◽  
Quan Z. Sheng ◽  
Xiaofei Xu ◽  
DianHui Chu ◽  
Wei Emma Zhang
Keyword(s):  
Edge Computing ◽  
Context Aware ◽  
Mobile Edge Computing ◽  
Qos Prediction ◽  
Computing Services ◽  
Adaptive Qos

scholarly journals Priority-Based Offloading and Caching in Mobile Edge Cloud

2019 ◽  
Vol 15 (2) ◽  
Author(s):  
Fernaz Narin Nur ◽  
Saiful Islam ◽  
Nazmun Nessa Moon ◽  
Asif Karim ◽  
Sami Azam ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Mobile Network ◽  
Edge Computing ◽  
Joint Optimization ◽  
Mobile Edge Computing ◽  
Stage Of Development ◽  
Research Initiative ◽  
Novel Concept ◽  
Reducing Energy ◽  
Task Offloading

Mobile Edge Computing (MEC) is relatively a novel concept in the parlance of Computational Offloading. MEC signifies the offloading of intensive computational tasks to the cloud which is generally positioned at the edge of a mobile network. Being in an embryonic stage of development, not much research has yet been done in this field despite its potential promises. However, with time the advantages are gaining growing attention and MEC is gradually taking over some of the resource-intensive functionalities of a traditional centralized cloud-based system. Another new idea called Task Caching is emerging rapidly with the offloading policy. This joint optimization idea of Task Offloading and caching is relatively a very new concept. It has been in use for reducing energy consumption and delay time for mobile edge computing. Due to the encouraging offshoots from some of the current research on the joint optimization problem, this research initiative aims to take the progress forward. The work improves upon the “prioritization of the tasks” by adopting a very practical approach discussed forward, and proposes a different way for Task Offloading and caching to the edge of the cloud, thereby bringing a significant enhancement to the QoS of MEC.

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 Modified reinforcement learning based- caching system for mobile edge computing

2020 ◽  
pp. 1-16
Author(s):  
Sarra Mehamel ◽  
Samia Bouzefrane ◽  
Soumya Banarjee ◽  
Mehammed Daoui ◽  
Valentina E. Balas
Keyword(s):  
Reinforcement Learning ◽  
Mobile Networks ◽  
Mobile Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Network Nodes ◽  
Caching Strategy ◽  
Edge Network ◽  
The Right ◽  
And Storage

Caching contents at the edge of mobile networks is an efficient mechanism that can alleviate the backhaul links load and reduce the transmission delay. For this purpose, choosing an adequate caching strategy becomes an important issue. Recently, the tremendous growth of Mobile Edge Computing (MEC) empowers the edge network nodes with more computation capabilities and storage capabilities, allowing the execution of resource-intensive tasks within the mobile network edges such as running artificial intelligence (AI) algorithms. Exploiting users context information intelligently makes it possible to design an intelligent context-aware mobile edge caching. To maximize the caching performance, the suitable methodology is to consider both context awareness and intelligence so that the caching strategy is aware of the environment while caching the appropriate content by making the right decision. Inspired by the success of reinforcement learning (RL) that uses agents to deal with decision making problems, we present a modified reinforcement learning (mRL) to cache contents in the network edges. Our proposed solution aims to maximize the cache hit rate and requires a multi awareness of the influencing factors on cache performance. The modified RL differs from other RL algorithms in the learning rate that uses the method of stochastic gradient decent (SGD) beside taking advantage of learning using the optimal caching decision obtained from fuzzy rules.

scholarly journals A Survey on Secure Deployment of Mobile Services in Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Mengmeng Cui ◽  
Yiming Fei ◽  
Yin Liu
Keyword(s):  
Autonomous Vehicles ◽  
Mobile Network ◽  
Edge Computing ◽  
Internet Of Vehicles ◽  
It Service ◽  
Computing Services ◽  
Service Deployment ◽  
Cloud Computing Services ◽  
Complete Survey ◽  
Research Studies

Mobile edge computing (MEC) is an emerging technology that is recognized as a key to 5G networks. Because MEC provides an IT service environment and cloud-computing services at the edge of the mobile network, researchers hope to use MEC for secure service deployment, such as Internet of vehicles, Internet of Things (IoT), and autonomous vehicles. Because of the characteristics of MEC which do not have terminal servers, it tends to be deployed on the edge of networks. However, there are few related works that systematically introduce the deployment of MEC. Also, secure service deployment frameworks with MEC are even rare. For this reason, we have conducted a comprehensive and concrete survey of recent research studies on secure deployment. Although numerous research studies and experiments about MEC service deployment have been conducted, there are few systematic summaries that conclude basic concepts and development strategies about secure service deployment of commercial MEC. To make up for the gap, a detailed and complete survey about relative achievements is presented.

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