Cache Performance Models for Quality of Service Compliance in Storage Clouds

Ernest Sithole, Aaron McConnell, Sally McClean, Gerard Parr, Bryan Scotney, Adrian Moore, David Bustard

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
44 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)Article 1-(24 pages)
JournalJournal of Cloud Computing: Advances, Systems and Applications
Issue number1
Publication statusPublished (in print/issue) - Dec 2013


  • Storage cloud
  • Enterprise applications
  • Cache performance
  • Optimisation


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