Admission Control of Video Sessions over Ad Hoc Networks Using Neural Classifiers

Vassis, Dimitris; Kampouraki, A.; Belsis, Petros; Skourlas, Christos

doi:10.1109/milcom.2014.173

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…As shown in Table 14, Admission control has leveraged ML extensively in a variety of networks, including ATM networks [95,189,190], wireless networks [8,36,359], cellular networks [66,67,281,372,458], ad hoc networks [452], and next generation networks [311]. To the best of our knowledge, Hiramatsu [189] was the first to propose NN based solutions controlling the admission of a service requesting resources for a basic call setup in ATM networks.…”

Section: Admission Controlmentioning

confidence: 99%

“…Vassis et al [452] propose an adaptive and distributed admission control mechanism for variable bitrate video sessions, over ad hoc networks with heterogeneous video and HTTP traffic. Unlike previous admission control approaches that only consider the new request, this mechanism takes into account the QoS constraints of all the services in the network.…”

Section: Admission Controlmentioning

confidence: 99%

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A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Boutaba

Salahuddin

Limam

et al. 2018

J Internet Serv Appl

794

391

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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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