Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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Inferring the quality of streaming video applications is important for Internet service providers, but the fact that most video streams are encrypted makes it difficult to do so. We develop models that infer quality metrics (\ie, startup delay and resolution) for encrypted streaming video services. Our paper builds on previous work, but extends it in several ways. First, the models work in deployment settings where the video sessions and segments must be identified from a mix of traffic and the time precision of the collected traffic statistics is more coarse (\eg, due to aggregation). Second, we develop a single composite model that works for a range of different services (\ie, Netflix, YouTube, Amazon, and Twitch), as opposed to just a single service. Third, unlike many previous models, our models perform predictions at finer granularity (\eg, the precise startup delay instead of just detecting short versus long delays) allowing to draw better conclusions on the ongoing streaming quality. Fourth, we demonstrate the models are practical through a 16-month deployment in 66 homes and provide new insights about the relationships between Internet "speed'' and the quality of the corresponding video streams, for a variety of services; we find that higher speeds provide only minimal improvements to startup delay and resolution.

Network virtualization enables the multi-tenancy concept and paves the way towards more advancements and innovation in the underlying infrastructure. With network virtualization, allocating resources to Virtual Networks (VNs) that represent tenants' requests emerges as a challenging problem. This problem is commonly known as the Virtual Network Embedding (VNE) problem, and its NP-Hard nature has drawn a lot of attention from the research community. A common feature in the existing work is that the type of communication in the VN requests was never characterized, assuming that they exhibit unicast communication only. In this paper, we motivate the importance of characterizing the type of communication in VN requests. We present a formal definition of the VNE problem for VNs with multicast communication. To the best of our knowledge, the multicast VNE problem has not been addressed in the frame of cloud computing, where the location of all the virtual machines in a given multicast VN is unknown. We propose a novel 3-steps heuristic to solve the multicast VNE problem with end-delay and delay variation constraints. Our numerical results prove the efficiency of our suggested approach over multiple metrics and against numerous embedding heuristics.