Edge Computing Assisted Adaptive Streaming Scheme for Mobile Networks

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.

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Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

Information ◽

10.3390/info10080259 ◽

2019 ◽

Vol 10 (8) ◽

pp. 259 ◽

Cited By ~ 2

Author(s):

Jie Yuan ◽

Erxia Li ◽

Chaoqun Kang ◽

Fangyuan Chang ◽

Xiaoyong Li

Keyword(s):

Cellular Networks ◽

Mobile Networks ◽

Network Architecture ◽

Social Trust ◽

Edge Computing ◽

Mobile Edge Computing ◽

Internet Technologies ◽

Architecture Model ◽

Security Issues ◽

Cooperative Relationship

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

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Classifying flows and buffer state for youtube's HTTP adaptive streaming service in mobile networks

Proceedings of the 9th ACM Multimedia Systems Conference on - MMSys '18 ◽

10.1145/3204949.3204955 ◽

2018 ◽

Cited By ~ 12

Author(s):

Dimitrios Tsilimantos ◽

Theodoros Karagkioules ◽

Stefan Valentin

Keyword(s):

Mobile Networks ◽

Adaptive Streaming ◽

Http Adaptive Streaming ◽

Streaming Service

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NFVMon: Enabling Multioperator Flow Monitoring in 5G Mobile Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2018/2860452 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Enrique Chirivella-Perez ◽

Juan Gutiérrez-Aguado ◽

Jose M. Alcaraz-Calero ◽

Qi Wang

Keyword(s):

Mobile Networks ◽

Communication Systems ◽

Edge Computing ◽

Mobile Edge Computing ◽

High Definition ◽

Flow Monitoring ◽

Fifth Generation ◽

New Generation ◽

Monitoring Architecture

With the advances of new-generation wireless and mobile communication systems such as the fifth-generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra-High-Definition video applications is becoming ever popular. These applications require real-time monitoring and processing to meet the mission-critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presents NFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposed NFVMon is integrated with the management plane of the Cloud Computing. NFVMon has been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid-sized infrastructure testbed.

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HTTP adaptive streaming with indoors-outdoors detection in mobile networks

2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) ◽

10.1109/infcomw.2017.8116457 ◽

2017 ◽

Cited By ~ 4

Author(s):

Sami Mekki ◽

Theodoros Karagkioules ◽

Stefan Valentin

Keyword(s):

Mobile Networks ◽

Adaptive Streaming ◽

Http Adaptive Streaming

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Privacy-Aware Online Task Offloading for Mobile-Edge Computing

Wireless Communications and Mobile Computing ◽

10.1155/2021/6622947 ◽

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.

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