The future of mobile cloud computing: Integrating cloudlets and Mobile Edge Computing

2016 ◽  
Author(s):  
Yaser Jararweh ◽  
Ahmad Doulat ◽  
Omar AlQudah ◽  
Ejaz Ahmed ◽  
Mahmoud Al-Ayyoub ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Edge Computing ◽  
Mobile Cloud ◽  
Mobile Edge Computing ◽  
The Future

Implementation of Docker for mobile edge computing embedded platform

2021 ◽  
pp. 440-454
Author(s):  
Durga Chouhan ◽  
Nilima Gautam ◽  
Gaurav Purohit ◽  
Rajesh Bhdada
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Cloud Service ◽  
Edge Computing ◽  
Mobile Cloud ◽  
Device Performance ◽  
Mobile Edge Computing ◽  
Memory Consumption ◽  
Open Platform ◽  
Embedded Platform

Some of the glitches in Mobile cloud computing are high latency, network bottleneck, and congestion. From a centralized to a decentralized computing scenario, Mobile Edge computing can transfer the computation to the edge, which decreases the response time for the application and increase the user experience. This paper assesses Docker as a medium for Edge Computing, a container-based technology. Docker can be seen as an open platform for designers and system managers to use Docker Engine, a portable, lightweight runtime and packaging tool to build, ship, run applications, and Docker hub. This cloud service distributes applications and mechanizing workflows. The key benefit is that, as soon as possible, Docker can get code checked and can fit into production. This paper will discuss Docker and present how this technology has overcome the earlier problems of virtualization with building and deploying large applications in two ways. We have implemented Docker of YOLO and AQM models on Jetson TX2 and compared both applications on Docker and Host OS. We can use dockers in two ways either we can use previously made dockers or build our own Docker according to our needs. In this paper, I have used the Docker image of YOLO and built my own Docker for AQM Model. These Docker containers' performance measured in this paper based on their device performance, memory consumption, boot time, and Docker performed very well.

scholarly journals Introduction

2021 ◽  
pp. 1-8
Author(s):  
Yan Zhang
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Edge Computing ◽  
Mobile Cloud ◽  
Mobile Edge Computing ◽  
Potential Applications

AbstractThis chapter first introduces the fundamental concepts of mobile cloud computing. The differences between mobile edge computing and mobile cloud computing are then discussed in detail. The European Telecommunications Standards Institute’s concept of mobile edge computing is introduced with respect to mobile edge computing’s definition, architecture, advantages, and potential applications.

scholarly journals Joint Computation Offloading and Data Caching Based on Cooperation of Mobile-Edge-Computing-Enabled Base Stations

2021 ◽  
Vol 11 (13) ◽  
pp. 5802
Author(s):  
Tian Liu ◽  
Wenhao Fan ◽  
Fan Wu ◽  
Wei Xie ◽  
Wen Yuan
Keyword(s):  
Cloud Computing ◽  
Mobile Cloud Computing ◽  
Edge Computing ◽  
Base Stations ◽  
Computation Offloading ◽  
Mobile Cloud ◽  
Mobile Edge Computing ◽  
Data Caching ◽  
Time Costs ◽  
And Storage

Mobile terminal applications with high computing complexity and high time delay sensitivity are developing quite fast today, which aggravates the load of mobile cloud computing and storage and further leads to network congestion and service quality decline. Mobile edge computing (MEC) is a way of breaking through the limits of computing and storage resources of mobile cloud and alleviating the load of mobile cloud. Computing time costs and transmission time costs are considered to be the main issues for the mobile cloud when carrying out computing offloading and data caching. Therefore, an efficient resource management strategy, which could minimize the system delay, is proposed in this paper. The new scheme offloads reasonably computing tasks and caches the tasks’ data from the mobile cloud to mobile edge computing-enabled base stations. An intelligence algorithm, genetic algorithm, is being used to solve the global optimization problem which would cause transmission delay and computing resources occupation, and to determine the computing offloading and data caching probability. The simulation of the system using MATLAB is conducted in 8 different scenarios with different parameters. The results show that our new scheme improves the system computing speed and optimizes the user experience in all scenarios, compared with the scheme without data caching and the scheme without computing offloading and data caching.

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