It starts to be widely recognized the need for application-level context visibility to properly perform streaming service management in wired-wireless integrated networks. In particular, the paper claims the need for full application-level awareness of context data about the IEEE 802.11 performance anomaly, i.e., when even a single node located at the borders of the coverage area of a Wi-Fi access point produces a relevant degradation in the connectivity quality of all other nodes in the area. We propose a middleware that, on the one hand, portably predicts and detects anomaly situations via decentralized and lightweight client-side mechanisms and, on the other hand, exploits anomaly awareness to promptly react with application-level management operations (streaming quality downscaling and traffic shaping). In particular, the paper focuses on how our middleware performs anomaly-driven quality downscaling both to preserve the goodput at nodes in well-covered areas and to minimize quality degradations at the clients generating the anomaly. The reported experimental results point out how anomaly prediction/detection can relevantly improve the effectiveness of streaming downscaling, thus allowing to maintain acceptable service quality notwithstanding Wi-Fi anomaly occurrences.

Dynamic and Context-aware Streaming Adaptation to Smooth Quality Degradation due to IEEE 802.11 Performance Anomaly

BELLAVISTA, PAOLO;CORRADI, ANTONIO;FOSCHINI, LUCA
2008

Abstract

It starts to be widely recognized the need for application-level context visibility to properly perform streaming service management in wired-wireless integrated networks. In particular, the paper claims the need for full application-level awareness of context data about the IEEE 802.11 performance anomaly, i.e., when even a single node located at the borders of the coverage area of a Wi-Fi access point produces a relevant degradation in the connectivity quality of all other nodes in the area. We propose a middleware that, on the one hand, portably predicts and detects anomaly situations via decentralized and lightweight client-side mechanisms and, on the other hand, exploits anomaly awareness to promptly react with application-level management operations (streaming quality downscaling and traffic shaping). In particular, the paper focuses on how our middleware performs anomaly-driven quality downscaling both to preserve the goodput at nodes in well-covered areas and to minimize quality degradations at the clients generating the anomaly. The reported experimental results point out how anomaly prediction/detection can relevantly improve the effectiveness of streaming downscaling, thus allowing to maintain acceptable service quality notwithstanding Wi-Fi anomaly occurrences.
P. Bellavista; A. Corradi; L. Foschini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/60530
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