High-performance flow classification using hybrid clusters in software defined mobile edge computing

2020 ◽  
Vol 160 ◽  
pp. 643-660 ◽  
Author(s):  
Mahdi Abbasi ◽  
Azad Shokrollahi ◽  
Mohammad R. Khosravi ◽  
Varun G. Menon
Keyword(s):  
High Performance ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Flow Classification ◽  
Performance Flow

scholarly journals Self-Adaptive Learning of Task Offloading in Mobile Edge Computing Systems

Entropy ◽  
2021 ◽  
Vol 23 (9) ◽  
pp. 1146
Author(s):  
Peng Huang ◽  
Minjiang Deng ◽  
Zhiliang Kang ◽  
Qinshan Liu ◽  
Lijia Xu
Keyword(s):  
Mobile Devices ◽  
Adaptive Learning ◽  
Urban Areas ◽  
High Performance ◽  
Dynamic Environment ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computationally Intensive ◽  
Task Offloading ◽  
Self Adaptive

Mobile edge computing (MEC) focuses on transferring computing resources close to the user's device, and it provides high-performance and low-delay services for mobile devices. It is an effective method to deal with computationally intensive and delay-sensitive tasks. Given the large number of underutilized computing resources for mobile devices in urban areas, leveraging these underutilized resources offers tremendous opportunities and value. Considering the spatiotemporal dynamics of user devices, the uncertainty of rich computing resources and the state of network channels in the MEC system, computing resource allocation in mobile devices with idle computing resources will affect the response time of task requesting. To solve these problems, this paper considers the case in which a mobile device can learn from a neighboring IoT device when offloading a computing request. On this basis, a novel self-adaptive learning of task offloading algorithm (SAda) is designed to minimize the average offloading delay in the MEC system. SAda adopts a distributed working mode and has a perception function to adapt to the dynamic environment in reality; it does not require frequent access to equipment information. Extensive simulations demonstrate that SAda achieves preferable latency performance and low learning error compared to the existing upper bound algorithms.

scholarly journals Cultural Distance-Aware Service Recommendation Approach in Mobile Edge Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Li ◽  
Yan Guo
Keyword(s):  
Big Data ◽  
High Performance ◽  
Cultural Distance ◽  
Edge Computing ◽  
User Preference ◽  
Mobile Edge Computing ◽  
Service Recommendation ◽  
Rating Data ◽  
Close Proximity ◽  
Real World Datasets

In the era of big data, traditional computing systems and paradigms are not efficient and even difficult to use. For high performance big data processing, mobile edge computing is emerging as a complement framework of cloud computing. In this new computing architecture, services are provided within a close proximity of mobile users by servers at the edge of network. Traditional collaborative filtering recommendation approach only focuses on the similarity extracted from the rating data, which may lead to an inaccuracy expression of user preference. In this paper, we propose a cultural distance-aware service recommendation approach which focuses on not only the similarity but also the local characteristics and preference of users. Our approach employs the cultural distance to express the user preference and combines it with similarity to predict the user ratings and recommend the services with higher rating. In addition, considering the extreme sparsity of the rating data, missing rating prediction based on collaboration filtering is introduced in our approach. The experimental results based on real-world datasets show that our approach outperforms the traditional recommendation approaches in terms of the reliability of recommendation.

scholarly journals Joint communication and computing resource allocation in vehicular edge computing

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983785 ◽  
Author(s):  
Jianan Sun ◽  
Qing Gu ◽  
Tao Zheng ◽  
Ping Dong ◽  
Yajuan Qin
Keyword(s):  
High Performance ◽  
Ad Hoc ◽  
High Reliability ◽  
Scheduling Algorithm ◽  
Computing System ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Wide Range ◽  
On The Road

The emergence of computation-intensive vehicle applications poses a significant challenge to the limited computation capacity of on-board equipments. Mobile edge computing has been recognized as a promising paradigm to provide high-performance vehicle services by offloading the applications to edge servers. However, it is still a challenge to efficiently utilize the available resources of vehicle nodes. In this article, we introduce mobile edge computing technology to vehicular ad hoc network to build a vehicular edge computing system, which provides a wide range of reliable services by utilizing the computing resources of vehicles on the road. Then, we study the computation offloading decision problem in this system and propose a novel multi-objective vehicular edge computing task scheduling algorithm which jointly optimizes the allocation of communication and computing resources. Extensive performance evaluation demonstrates that the proposed algorithm can effectively shorten the task execution time and has high reliability.

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing

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

Communication-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks

2020 ◽  
pp. 1-1
Author(s):  
Ping Zhou ◽  
Ke Shen ◽  
Neeraj Kumar ◽  
Yin Zhang ◽  
Mohammad Mehedi Hassan ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Edge Computing ◽  
Mobile Edge Computing
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