Regression Analysis of Predictions and Forecasts of Cloud Data Centre KPIs Using the Boosted Decision Tree Algorithm

Abstract

<div>The National Institute of Standards and Technology defines the fundamental characteristics of cloud computing as: on-demand computing, offered via the network, using pooled resources, with rapid elastic scaling and metered charging. The rapid dynamic allocation and release of resources on demand to meet heterogeneous computing needs is particularly challenging for data centres, which process a huge amount of data characterised by its high volume, velocity, variety and veracity (4Vs model). Data centres seek to regulate this by monitoring and adaptation, typically reacting to service failures after the fact. We present a real cloud test bed with the capabilities of proactively monitoring and gathering cloud resource information for making predictions and forecasts. This contrasts with the state-of-the-art reactive monitoring of cloud data centres. We argue that the behavioural patterns and Key Performance Indicators (KPIs) characterizing virtualized servers, networks, and database applications can best be studied and analysed with predictive models. Specifically, we applied the Boosted Decision Tree machine learning algorithm in making future predictions on the KPIs of a cloud server and virtual infrastructure network, yielding an R-Square of 0.9991 at a 0.2 learning rate. This predictive framework is beneficial for making short- and long-term predictions for cloud resources.</div>

Kalman Filter Based Prediction and Forecasting of Cloud Server KPIs

“…Our approach in this work applies the state space version of the Kalman models in building predictive models that can be used for analyzing and forecasting cloud resources allocation and consumption. To further state the benefits of our approach, we present in section 6.4 a detailed comparative analysis using two machine learning algorithms (stochastic gradient decent (SGD ) and boosted decision tree) from previous work in [23] and a reactive approach developed by [24].…”

“…The model built on the time series training set example does not require an input vector to drive the dynamics of the output variable. Therefore (23) describes a modification with emphasis on the noise as a result of the state estimates multiplied with a Gamma (γ ) constant factor. The noise of the observations is characterized by only delta (δ k ) which is consistent with the definition of the observation equation (1).…”

“…In order to quantify and state the benefits of our approach with the Kalman filtering techniques, we compared this work with previous works using two machine learning algorithms (the boosted decision tree(BDT) and stochastic gradient descent (SGD)) [23] and the reactive approach in [24].…”