PDF Publication Title:
Text from PDF Page: 150
and particular devices (e.g. IoT) and provide real-time local data analysis at the edge of the network, near the source of the data. The capabilities provided by edge computing can be improved if they are leveraged using real-time QoE estimates. This is even more valuable for video transmission networks whose real-time nature makes more important a rapid reaction to QoE degradation [1,2,3,4]. Fig. 1 shows an abstract view of data distribution and processing services for IoT applications. The cloud/central services are responsible for application management and overall coordination. The end devices (IoT devices) produce and consume operational data and commands. Finally, the distribution/edge processing services (middle layer in Fig. 1) are intended to facilitate communication, increase availability and performance and add distributed services closer to the end devices. This middle layer can host services that would otherwise be difficult to deploy at the cloud location (slow and unreliable access) or at the IoT devices (lacking processing capabilities). The QoE predictor proposed here is intended to be deployed as a quality monitoring service at the edge processing layer in Fig. 1. Fig. 1. Abstract view of data distribution and processing services for IoT applications As the demand for video services increases in parallel with the storage and processing capabilities at the edge layer [3], it is now possible to host highly demanding video processing services in this layer, which allows to offer new network capacities based on automatic and intelligent analysis of video transmissions and QoE-aware network management and video traffic prioritization and scheduling [1,2,3,6]. Hence the importance of more robust and accurate QoE predictors that can make better use of the new processing platforms (e.g. GPUs) at the edge layer [3]. Our proposed predictor is based in a deep learning model that is especially suitable for these new platforms. At present, the usual way to evaluate QoE is either to carry out experiments with individuals as testers or to calculate it indirectly from Quality of Service (QoS) network parameters (jitter, delay, packet loss,..) [4,5,6]. Another approach, recently being actively explored is applying machine learning (ML) to video QoE estimation. The resulting QoE detector is able to predict QoE directly from information contained in the transmitted videos, the network packets or end- user recorded events (e.g. related web activity). This approach is the one taken on this work in order to build a video QoE detector from network packets information using deep learning Doctoral Thesis: Novel applications of Machine Learning to NTAP - 148PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
PDF Search Title:
Novel applications of Machine Learning to Network Traffic AnalysisOriginal File Name Searched:
456453_1175348.pdfDIY PDF Search: Google It | Yahoo | Bing
Cruise Ship Reviews | Luxury Resort | Jet | Yacht | and Travel Tech More Info
Cruising Review Topics and Articles More Info
Software based on Filemaker for the travel industry More Info
The Burgenstock Resort: Reviews on CruisingReview website... More Info
Resort Reviews: World Class resorts... More Info
The Riffelalp Resort: Reviews on CruisingReview website... More Info
| CONTACT TEL: 608-238-6001 Email: greg@cruisingreview.com | RSS | AMP |