scholarly journals An Adaptive Replica Configuration Mechanism Based on Predictive File Popularity and Queue Balance in Mobile Edge Computing Environment

2021 ◽  
Author(s):  
Mao-Lun Chiang ◽  
Hui-Ching Hsieh ◽  
Ting-Yi Chang ◽  
Wei-Ling Lin ◽  
Hong-Wei Chen
Keyword(s):  
Access Network ◽  
Edge Computing ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Radio Access ◽  
Mobile Big Data ◽  
Short Period ◽  
Network Delays ◽  
Cloud Network ◽  
And Storage

Abstract In the current era of the Internet of Things (IoT), various devices can provide more services by connecting to the Internet. However, the explosive growth of connected devices will cause the cloud core overload and significant network delays. To overcome these problems, the Mobile Edge Computing (MEC) network is proposed to provide most of the computing and storage near the radio access network to reduce the traffic of the core cloud network and provide lower latency for the terminal.Mobile edge computing can work with third parties to develop multiple services, such as mobile big data analysis and context-aware services. However, when there is a large amount of popular data accessed in a short period, the system must generate many replicas, which will not only reduce access efficiency but also cause additional traffic overhead. To improve the above problems, an Adaptive Replica Configuration Mechanism (ARCM) is proposed in this paper to predict the popularity of the file and make a replica to the low-blocking node. This method spreads the subsequent access workload by copying the popular file in advance to improve the overall performance of the system.

scholarly journals Zone-Based Cooperative Content Caching and Delivery for Radio Access Network With Mobile Edge Computing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4031-4044 ◽  
Author(s):  
Ning Wang ◽  
Gangxiang Shen ◽  
Sanjay Kumar Bose ◽  
Weidong Shao
Keyword(s):  
Access Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Radio Access Network ◽  
Content Caching ◽  
Radio Access

scholarly journals Communication and computation cooperation in cloud radio access network with mobile edge computing

2018 ◽  
Vol 2 (1) ◽  
pp. 43-56
Author(s):  
Tong Li ◽  
Kezhi Wang ◽  
Ke Xu ◽  
Kun Yang ◽  
Chathura Sarathchandra Magurawalage ◽  
...  
Keyword(s):  
Access Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Radio Access Network ◽  
Cloud Radio Access Network ◽  
Radio Access

scholarly journals An Intelligent Real-Time Traffic Control Based on Mobile Edge Computing for Individual Private Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Sa Math ◽  
Lejun Zhang ◽  
Seokhoon Kim ◽  
Intae Ryoo
Keyword(s):  
Real Time ◽  
Optical Networks ◽  
Traffic Control ◽  
Access Network ◽  
Computer Software ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Great Opportunity ◽  
Real Time Traffic ◽  
Radio Access

The existence of Mobile Edge Computing (MEC) provides a novel and great opportunity to enhance user quality of service (QoS) by enabling local communication. The 5th generation (5G) communication is consisting of massive connectivity at the Radio Access Network (RAN), where the tremendous user traffic will be generated and sent to fronthaul and backhaul gateways, respectively. Since fronthaul and backhaul gateways are commonly installed by using optical networks, the bottleneck network will occur when the incoming traffic exceeds the capacity of the gateways. To meet the requirement of real-time communication in terms of ultralow latency (ULL), these aforementioned issues have to be solved. In this paper, we proposed an intelligent real-time traffic control based on MEC to handle user traffic at both gateways. The method sliced the user traffic into four communication classes, including conversation, streaming, interactive, and background communication. And MEC server has been integrated into the gateway for caching the sliced traffic. Subsequently, the MEC server can handle each user traffic slice based on its QoS requirements. The evaluation results showed that the proposed scheme enhances the QoS and can outperform on the conventional approach in terms of delays, jitters, and throughputs. Based on the simulated results, the proposed scheme is suitable for improving time-sensitive communication including IoT sensor’s data. The simulation results are validated through computer software simulation.

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 Intelligent scaling for 6G IoE services for resource provisioning

2021 ◽  
Vol 7 ◽  
pp. e755
Author(s):  
Abdullah Alharbi ◽  
Hashem Alyami ◽  
Poongodi M ◽  
Hafiz Tayyab Rauf ◽  
Seifedine Kadry
Keyword(s):  
Reinforcement Learning ◽  
Resource Provisioning ◽  
Edge Computing ◽  
Effective Model ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Placement Decisions ◽  
Service Placement

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

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