Monitoring and Adaptation of Pooled Cloud Computing Resources

Cloud computing service providers seek to deploy vast quantities of shared resources that are managed in an optimal way for all consumers. Monitoring describes a collection of techniques for measuring cloud resource consumption and performance, and for detecting anomalies. Adaptation describes a collection of techniques for effecting changes to a cloud configuration in order to improve performance, or counter anomalous behaviour. The state-of-the-art in cloud monitoring mostly generates statistics about Infrastructureas- a-Service (IaaS) which is used privately to inform the providers about the health of the service. Consumers see other aspects, such as service latency and response-times, affected by numbers of tenants on a given platform. While cloud providers offer Service Level Agreements (SLAs), it is often not possible to tell whether these are being honoured. Providers want to predict performance, plan capacity and detect anomalies well in advance; and consumers need guarantees that their SLAs are being honoured. Our approach presented a real cloud testbed with the capabilities of proactively monitoring and gathering cloud resources information for making prediction and forecasting. As part of this, new adaptive filtering approaches were explored at the heart of the feedback driven control algorithms. This research investigated the use of several adaptive filtering techniques, including the Least Mean Square (LMS) as a benchmark, but later focused on the Kalman optimal H2 filter, and the boosted decision tree regression machine learning method, as possibly useful algorithms for tracking resource consumption and making predictions about future resource usage. Our results show that two regression methods can model the problem of cloud resource prediction based on past observations. The Boosted decision tree produced the most suitable predictions based on the feature labels such as the number of virtual users. Our investigations also reveal that the optimal Kalman filter can predict and forecast cloud resource consumptions into a future time horizon accurately. We also show that this predictive analytic framework is inline with the concept of the MAPE-K in autonomous computing.