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<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
In such a competitive world, quality assurance can make the difference between a successful business and bankruptcy. For Internet services, the presence of low performance servers, high latency or overall poor service quality can translate into lost sales, user frustration and customers lost. In this paper, we propose a novel method for QoS metrification based on Hidden Markov Models. The techniques we show can be used to measure and predict the behavior of Web Services under several criteria,<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-40820-5_33">doi:10.1007/978-3-642-40820-5_33</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rkqzlv6d55ewfe52yfyt6lcewq">fatcat:rkqzlv6d55ewfe52yfyt6lcewq</a> </span>
more »... nd can thus be used to rank services quantitatively rather than just qualitatively. We demonstrate the feasibility and usefulness of our methodology by drawing experiments on real world data. Our results have shown how our proposed methods can help the user to automatically select the best available Web Service based on several metrics, among them system predictability and response times variability.
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