2013
DOI: 10.1145/2491465.2491468
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Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee

Abstract: Autonomic server provisioning for performance assurance is a critical issue in Internet services. It is challenging to guarantee that requests flowing through a multi-tier system will experience an acceptable distribution of delays. The difficulty is mainly due to highly dynamic workloads, the complexity of underlying computer systems, and the lack of accurate performance models. We propose a novel autonomic server provisioning approach based on a model-independent self-adaptive Neural Fuzzy Control (NFC). Exi… Show more

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Cited by 28 publications
(25 citation statements)
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“…Lastly, they are also criticized more often for their associated unwanted behaviour, termed as bumpy transition, that could lead the system to an oscillatory state [27,104,105]. On the other hand, knowledge-based control solutions utilizing machine learning [67,106,107] or neural networks [99,108] provide high levels of flexibility and adaptivity. However, such flexibility and adaptivity come at the cost of long training delays, poor scalability, slower convergence rate, and the impossibility of deriving stability proof [7,18,103,109].…”
Section: Discussion Issues and Challengesmentioning
confidence: 99%
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“…Lastly, they are also criticized more often for their associated unwanted behaviour, termed as bumpy transition, that could lead the system to an oscillatory state [27,104,105]. On the other hand, knowledge-based control solutions utilizing machine learning [67,106,107] or neural networks [99,108] provide high levels of flexibility and adaptivity. However, such flexibility and adaptivity come at the cost of long training delays, poor scalability, slower convergence rate, and the impossibility of deriving stability proof [7,18,103,109].…”
Section: Discussion Issues and Challengesmentioning
confidence: 99%
“…Wang et al [77], in contrast focused on data based system to readjustment the CPU capacity and disk IO bandwidth using an adaptive fuzzy modelling approach. Some other examples of fuzzy approaches include neural fuzzy control [99], fuzzy logic based feedback controller [100], fuzzy model coupled with a performance prediction model [101] and multi-agent fuzzy control [102].…”
Section: Intelligentmentioning
confidence: 99%
“…Fuzzy controllers has been applied in the context of virtulized resource management [Xu et al 2007;Rao et al 2011;Lama and Zhou 2013] and cloud computing [Wang et al 2015;Jamshidi et al 2014]. For instance, the inputs to such controllers may include the workload level (w) and response time (rt) and the output may be the scaling action (sa) in terms of increment (or decrement) in the number of VMs.…”
Section: Knowledge-based Controllersmentioning
confidence: 99%
“…For the definition of the functions in the rule consequents, the knowledge and experience of a human expert is generally used. In the situations where no a priori knowledge for defining such rules is assumed, a learning mechanism can be instead adopted [Lama and Zhou 2013;Jamshidi et al 2016].…”
Section: Knowledge-based Controllersmentioning
confidence: 99%
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