19 research outputs found
A hybrid neuro--wavelet predictor for QoS control and stability
For distributed systems to properly react to peaks of requests, their
adaptation activities would benefit from the estimation of the amount of
requests. This paper proposes a solution to produce a short-term forecast based
on data characterising user behaviour of online services. We use \emph{wavelet
analysis}, providing compression and denoising on the observed time series of
the amount of past user requests; and a \emph{recurrent neural network} trained
with observed data and designed so as to provide well-timed estimations of
future requests. The said ensemble has the ability to predict the amount of
future user requests with a root mean squared error below 0.06\%. Thanks to
prediction, advance resource provision can be performed for the duration of a
request peak and for just the right amount of resources, hence avoiding
over-provisioning and associated costs. Moreover, reliable provision lets users
enjoy a level of availability of services unaffected by load variations
Modeling Distributed Applications for QoS Management
To increase the total QoS (Quality of Service) provided by a system, run-time adaptation of application behavior to execution context is a promising way. This paper focuses on modeling of distributed applications in order to manage their QoS through a middleware. The model captures the information required to schedule application activities in order to improve QoS and share out resources. Since only applications hold the required knowledge for alternative execution or graceful degradation, they adapt themselves under the control of the middleware. To ease the design of applications, a virtual execution environment implements the proposed middleware and emulates the execution of applications from their model
Web server QoS management by adaptive content delivery
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