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

SIGLEAvailable from British Library Document Supply Centre-DSC:4335.26205(1999-161) / BLDSC - British Library Document Supply CentreGBUnited Kingdo