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Proceedings ArticleDOI

Application-Aware Traffic Redirection: A Mobile Edge Computing Implementation Toward Future 5G Networks

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TLDR
This paper presents the prototype implementation of a MEC platform by developing an application-aware traffic redirection mechanism at edge network to reduce service latency and network bandwidth consumption and is one of the few MEC solutions built for 5G networks in practice.
Abstract
With the development of network technology, there are billions of devices accessing resources and services on the cloud through mobile telecommunication network. A great number of connections and data packets must be handled by the mobile network. It not only consumes the limited spectrum resources and network bandwidth, but also reduces the service quality of applications. To alleviate the problem, the concept of Mobile Edge Computing (MEC) has been proposed by European Telecommunications Standard Institute (ETSI) in 2014. MEC suggests to provide IT and cloud computing capabilities at the network edge to offer low-latency and high-bandwidth service. The architecture and the benefits of MEC have been discussed in many recent literature. But the implementation of underlying network is rarely discussed or evaluated in practice. In this paper, we present our prototype implementation of a MEC platform by developing an application-aware traffic redirection mechanism at edge network to reduce service latency and network bandwidth consumption. Our implementation is based on OAI, an open source project of 5G SoftRAN cellular system. To the best of our knowledge, it is also one of the few MEC solutions that have been built for 5G networks in practice.

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A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

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Recent Advances of Edge Cache in Radio Access Networks for Internet of Things: Techniques, Performances, and Challenges

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References
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Journal ArticleDOI

Replisom : Disciplined Tiny Memory Replication for Massive IoT Devices in LTE Edge Cloud

TL;DR: The analysis and numerical evaluation suggest that the proposed Replisom system has significant potential in reducing the delay, energy consumption, and cost for cloud offloading of IoT applications given the massive number of devices with tiny memory sizes.
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ME-VoLTE: Network functions for energy-efficient video transcoding at the mobile edge

TL;DR: This paper introduces an approach to reduce power consumption of mobile phones by offloading video encoding efforts from mobile devices to external services hosted on servers co-located with cellular base stations.
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Minimizing Latency of Real-Time Container Cloud for Software Radio Access Networks

TL;DR: This work empirically study the problem of minimizing computational and networking latencies in lightweight container cloud and shows the feasibility of running software RANs in real-time container cloud.
Proceedings ArticleDOI

OpenBTS: A Step Forward in the Cognitive Direction

TL;DR: The proposed spectrum sensing strategy allows the OpenBTS system to be aware of other active transmissions by forcing to choose a new radio channel within the GSM frequency band, when a licensed primary user has to transmit on a busy channel.
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