FLHyO: fuzzy logic based hybrid overlay for P2P live video streaming

Pal, Kunwar; Govil, Mahesh Chandra; Ahmed, Mushtaq

doi:10.1007/s11042-019-08010-4

Cited by 9 publications

(3 citation statements)

References 33 publications

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“…A similar strategy has been used in hybrid P2P-CDN overlay [10]. Fuzzy Logic based Hybrid Overlay (FLHyO) is proposed in [11] that utilizes the fuzzy system to find out the priority of all the peers during overlay construction by considering upload bandwidth, geographical location, age and utilization of a peer. Ma et al [12] analyze a P2P-CDN based system and propose a peer selection strategy involving machine learning instead of localization.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-based Stable P2P IPTV Overlay

Iqbal¹,

Ullah²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of issues. Stability of peers in a network is one of the major issues among these. Most of the existing approaches address this issue through older-stable principle. This paper first extensively investigates the older-stable principle to observe its validity in different scenarios. It is observed that the older-stable principle does not hold in several of them. Then, it utilizes machine learning approach to predict the stability of peers. This work evaluates the accuracy of several machine learning algorithms over the prediction of stability, where the Gradient Boosting Regressor (GBR) out-performs other algorithms. Finally, this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay. The results indicate that machine learning-based stability estimation significantly improves the system.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-based Stable P2P IPTV Overlay

Iqbal¹,

Ullah²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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“…The goal of using machine learning is to restrict the cloud rental cost according to a desired QoS level. To construct a P2P IPTV overlay, [27] uses fuzzy logic. They define a metric "priority" to choose a parent peer which is a combination of the peer's age, upload bandwidth, and utilization.…”

Section: Related Workmentioning

confidence: 99%

“…Analysis-based works [14] Influential factors of peer's stability and bandwidth Global phenomenon [15] Arrivals and departures of users Ignore other factors such as streaming quality [16] Stability in mobile and fixed nodes Global behavior and missing factors [18] Impact of device type and connection type Global behavior and missing factors [19] Difference in mobile and nonmobile cases Global behavior and missing factors [20] Impact of download speed on user engagement Ignore other factors Model-based works [21] Contextual model based on machine learning Long learning time [22] Prediction of next channel Ignores other metrics [23] Prediction of next channel Ignores other metrics [24] Prediction of next channel Ignores other metrics [25] Prediction of neighbor's QoS Ignores other metrics [27] Priority: age, bandwidth, utilization Considers only age for stability 3 Wireless Communications and Mobile Computing 3.2. Crawlers Method.…”

Section: Ref Focus Limitationmentioning

confidence: 99%

Behavior Dissection of NGWN Live Audio and Video Streaming Users with Enhanced and Efficient Modelling

Ali

Ullah

Ahmed

et al. 2022

Wireless Communications and Mobile Computing

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Peer-to-peer (P2P) live video streaming is an application-level approach providing ease of deployment with low cost as compared to the IP multicast and client/server (C/S) architecture. These systems solely rely on end-hosts to disseminate the content; therefore, their performance largely banks on end-hosts, called peers. Since peers themselves are controlled by users, users’ activities become activities of peers. In such a network, the highly dynamic behavior of users impacts the network performance. Therefore, for performance improvement, a thorough understanding of user behavior is crucial. To explore and understand user behavior, numerous studies have been carried out. However, user behavior is complex, having several elements with dependency relationships, which make it difficult for a single measurement study to represent it comprehensively. Therefore, this work takes a two-step approach. Firstly, it collects existing measurement studies and analyzes, compares, and contrasts them to extract user behavior metrics and their relationships from them. Secondly, in light of the observations gained, this research analyzes traces of user behavior collected from an operational system. Such an outcome is useful, on one hand, for user behavior modelling towards performance improvement in NGWN, and on the other hand, it provides insights for further measurements and analysis.

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