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• W1981330285 abstract "The Proportional Slowdown Differentiation (PSD) is an important Quality of Service (QoS) model for services in the Cloud. It aims to maintain consistent pre-specified slowdown ratios between the requests of different service classes. In this paper, a novel online fuzzy learning algorithm is proposed to facilitate the PSD control for distributed services. The advantages of the proposed approach are self-optimization of its fuzzy rules and robust control performance. We built a prototype to realize the fuzzy learning algorithm and the PSD control mechanism based on an open source middleware platform. Comprehensive experiments over a wide range of parameters and workloads have been conducted. Experimental results show that the proposed fuzzy learning algorithm can achieve superior performance over several other controllers." @default.
• W1981330285 created "2016-06-24" @default.
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• W1981330285 creator A5062521849 @default.
• W1981330285 date "2011-12-01" @default.
• W1981330285 modified "2023-09-24" @default.
• W1981330285 title "SOSOF: A fuzzy learning approach for Proportional Slowdown Differentiation control" @default.
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• W1981330285 doi "https://doi.org/10.1109/soca.2011.6166241" @default.
• W1981330285 hasPublicationYear "2011" @default.
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