Towards effective dynamic resource allocation for enterprise applications

The growing use of online services requires substantial supporting infrastructure. The efficient deployment of applications relies on the cost effectiveness of commercial hosting providers who deliver an agreed quality of service as governed by a service level agreement for a fee. The priorities of the commercial hosting provider are to maximise revenue, by delivering agreed service levels, and minimise costs, through high resource utilisation. In order to deliver high service levels and resource utilisation, it may be necessary to reorganise resources during periods of high demand. This reorganisation process may be manual or alternatively controlled by an autonomous process governed by a dynamic resource allocation algorithm. Dynamic resource allocation has been shown to improve service levels and utilisation and hence, profitability. In this thesis several facets of dynamic resource allocation are examined to asses its suitability for the modern data centre. Firstly, three theoretically derived policies are implemented as a middleware for a modern multi-tier Web application and their performance is examined under a range of workloads in a real world test bed. The scalability of state-of-the art resource allocation policies are explored in two dimensions, namely the number of applications and the quantity of servers under control of the resources allocation policy. The results demonstrate that current policies presented in the literature demonstrate poor scalability in one or both of these dimensions. A new policy is proposed which has significantly improved scalability characteristics and the new policy is demonstrated at scale through simulation. The placement of applications in across a datacenter makes them susceptible to failures in shared infrastructure. To address this issue an application placement mechanism is developed to augment any dynamic resource allocation policy. The results of this placement mechanism demonstrate a significant improvement in the worst case when compared to a random allocation mechanism. A model for the reallocation of resources in a dynamic resource allocation system is also devised. The model demonstrates that the assumption of a constant resource reallocation cost is invalid under both physical reallocation and migration of virtualised resources

Abstract Predicting the Upper Bound of Web Traffic Volume Using a Multiple Time Scale Approach

This paper presents a prediction algorithm for estimating the upper bound of future Web traffic volume. Unlike traditional traffic predictions that are performed at a single time scale using curve fitting, we employ a multiple time scale approach and utilize traffic statistical properties to do forecasting. We have applied our prediction algorithm to the 1998 World Cup data set. Experiments show that it is effective for short term traffic bound predictions, applicable to bursty traffic, and useful for Web server overload prevention