Predicting Violations of QoS Requirements in Distributed Systems

Taylor, Sandra L.; Lutfiyya, Hanan

doi:10.1007/978-3-540-39404-4_27

Cited by 2 publications

(1 citation statement)

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“…Such a system continuously adjusts resource allocation to multimedia applications until their QoS requirements are fulfilled. In this way, QoS violations detection rules and thresholds can be derived through experimentation [23]. Comparatively, our work focuses on identifying a QoS violation through comprehensive observation of end-to-end QoS and traffic pattern.…”

Section: Overview Of Our Approachmentioning

confidence: 99%

Identifying QoS violations through statistical end‐to‐end analysis

Zhou

Chen

Pung

et al. 2011

Int J Communication

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SUMMARYMultimedia transmission over the network is susceptible to various runtime impairments such as process failure, network congestion or link error. Existing work usually determines such quality of service (QoS) violations through condition-action rules, which trigger corresponding actions once pre-described conditions are satisfied. However, the results of such rigid rules are often not satisfactory in practice in that there has been little serious study with respect to the relationship between the root cause of a QoS violation and the observed violation phenomenon.In this paper, we introduce a statistical approach to the analysis of QoS violations. We propose and validate through experiments that: (1) a type of QoS violation will present consistent symptoms in terms of the observed application performance and end-to-end traffic pattern. Such a violation can be recognized once the similar symptoms repeat during a QoS session. (2) QoS violations of different nature (e.g. caused by shortage of different resources) will present diverse symptoms. Using a set of end-to-end statistics, we are able to describe and differentiate between QoS violations. We propose a fast orthonormal algorithm for real-time training/classification of QoS violations and prove that this algorithm is universal approximation. We also extend the scope of hidden neurons from kernel functions to additive functions for higher classification accuracy. Our experiments validate our ideas and show the effectiveness of our approach in terms of training overhead and classification accuracy.

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Section: Overview Of Our Approachmentioning

confidence: 99%