With the growing popularity of cloud-based data centres as the enterprise IT platform of choice, there is a need for effective management strategies capable of maintaining performance within SLA and QoS parameters when responding to dynamic conditions such as increasing demand. Since current management approaches in the cloud infrastructure, particularly for data-intensive applications, lack the ability to systematically quantify performance trends, static approaches are largely employed in the allocations of resources when dealing with volatile demand in the infrastructure. We present analytical models for characterising cache performance trends at storage cache nodes. Practical validations of cache performance for derived theoretical trends show close approximations between modelled characterisations and measurement results for user request patterns involving private datasets and publicly available datasets. The models are extended to encompass hybrid scenarios based on concurrent requests of both private and public content. Our models have potential for guiding (a) efficient resource allocations during initial deployments of the storage cloud infrastructure and (b) timely interventions during operation in order to achieve scalable and resilient service delivery.

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Monitoring application performance over IT infrastructure is common practise for cloud providers, and is the foundation for ensuring the health of systems and quality of service for users. However, with the architecture of cloud computing allowing the allocation and provision of resources to be set at several layers, the scope for potential performance issues is increased and as a result increases the time and effort required to detect any issues. Cloud implementations often mean a reduction in IT staff, who are traditionally responsible for problem solving. Therefore monitoring must become automatic, dynamic and proactive compared to responsive and traditional 'fire fighting' approaches. In this paper, we look at the hierarchy of cloud computing and its effects on monitoring. With Cloud computing having several levels where resources can be allocated, as a result these can also form restrictions on the system, this increases the number of problem areas that support teams have to monitor, to avoid adverse effects on other parts of the cloud architecture where conflicts for resources can occur between applications and at different segments of the hierarchy.