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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Performance evaluations for enterprise applications running over IT systems are difficult to carry out given the multiplicity and variability of the operational components that constitute the dispersed IT infrastructures. To overcome this challenge, most of the approaches for performance assessment employ benchmarking strategies. While benchmarking methods provide exact indications on the performance capability of the measured facility, the results so obtained mostly apply to specific physical implementations considered in benchmark runs. The information provided by benchmark data thus restricts the ability to carry out meaningful performance analysis unless wide varieties of physical scenarios are generated for comparative studies. Given the logistical drawbacks associated with benchmarking techniques, we therefore propose a flexible model-based approach to determine quantitative performance for applications in IT systems by producing a range of performance models through the use of generic components that are easily assembled in simulation environments. Our approach initially considers a Tier 2 model framework whose components are derived from the SAP Sell-from-Stock application routine running on a multi-core processor server. The modelled framework is extensible enough to provide the definitions of resource consumptions patterns of different applications as well as the variety of server hardware systems. The simulations of our initial models developed so far generate results that are comparable to measurements obtained for scenarios in the Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SimulationWorks2010, March 15 -19, Torremolinos, Malaga, Spain Copyright 2010 low and moderate loading levels.

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