A hybrid neuro--wavelet predictor for QoS control and stability

TL;DR

This paper introduces a hybrid approach combining wavelet analysis and recurrent neural networks to accurately forecast user request volumes in online services, enabling better resource management and improved service stability.

Contribution

The paper presents a novel hybrid neuro-wavelet model that enhances short-term request prediction accuracy for distributed systems.

Findings

Prediction error below 0.06% RMSE

Enables precise resource provisioning during request peaks

Improves service availability and reduces over-provisioning costs

Abstract

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.