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Full-Text Articles in Electrical and Electronics
Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin
Quantum Classifiers For Video Quality Delivery, Tautvydas Lisas, Ruairí De Fréin
Conference papers
Classical classifiers such as the Support Vector Classifier (SVC) struggle to accurately classify video Quality of Delivery (QoD) time-series due to the challenge in constructing suitable decision boundaries using small amounts of training data. We develop a technique that takes advantage of a quantum-classical hybrid infrastructure called Quantum-Enhanced Codecs (QEC). We evaluate a (1) purely classical, (2) hybrid kernel, and (3) purely quantum classifier for video QoD congestion classification, where congestion is either low, medium or high, using QoD measurements from a real networking test-bed. Findings show that the SVC performs the classification task 4% better in the low congestion …
Load-Adjusted Prediction For Proactive Resource Management And Video Server Demand Profiling, Obinna Izima, Ruairí De Fréin
Load-Adjusted Prediction For Proactive Resource Management And Video Server Demand Profiling, Obinna Izima, Ruairí De Fréin
Articles
To lower costs associated with providing cloud resources, a network manager would like to estimate how busy the servers will be in the near future. This is a necessary input in deciding whether to scale up or down computing requirements. We formulate the problem of estimating cloud computational requirements as an integrated framework comprising of a learning and an action stage. In the learning stage, we use Machine Learning (ML) models to predict the video Quality of Delivery (QoD) metric for cloud-hosted servers and use the knowledge gained from the process to make resource management decisions during the action stage. …
Predicting Quality Of Delivery Metrics For Adaptive Video Codec Sessions, Obinna Izima, Ruairí De Fréin, Mark Davis
Predicting Quality Of Delivery Metrics For Adaptive Video Codec Sessions, Obinna Izima, Ruairí De Fréin, Mark Davis
Conference papers
Predicting video quality will continue to be an active area of research given the dominance of video traffic for years to come. Network service practitioners that are poised to handle the strain on the existing limited bandwidth constraints are better placed to be SLA-compliant. The dynamic and time-varying nature of cloud-hosted services require improved techniques to realize accurate models of the systems. To address this challenge: (1) we propose Codec-aware Network Adaptation Agent (cNAA), an online light-weight data learning engine that achieves accurate and correct predictions of quality of delivery (QoD) metrics, namely jitter for video services. cNAA achieves this …