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- W1991700115 abstract "Cloud computing is an internet based computing and is becoming popular. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocation QoS ranking prediction framework is used. This framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are used to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The proposed uses modernized ranking approach which uses different QoS parameters to predict the ranking more accurately. Different QoS parameters like latency, availability, failure probability can be used to improve the ranking. Keyword: Quality-of-service, cloud service, ranking prediction, personalization" @default.
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- W1991700115 date "2014-01-01" @default.
- W1991700115 modified "2023-10-14" @default.
- W1991700115 title "Ranking of Cloud Services Using Dynamic Qos" @default.
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- W1991700115 doi "https://doi.org/10.9790/0661-16285962" @default.
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