Today the cloud-desktop service, or Desktop-as-a-Service (DaaS), is massively replacing Virtual Desktop Infrastructures (VDI), as confirmed by the importance of players entering the DaaS market. In this paper we study the workload of a DaaS provider, analyzing three months of real traffic and resource usage. What emerges from the study, the first on the subject at the best of our knowledge, is that the workload on CPU and disk usage are long-tail distributed (lognormal, weibull and pareto) and that the length of working sessions is exponentially distributed. These results are extremely important for: the selection of the appropriate performance model to be used in capacity planning or run-time resource provisioning; the setup of workload generators; and the definition of heuristic policies for resource provisioning. The paper provides an accurate distribution fitting for all the workload features considered and discusses the implications of results on performance analysis.

Casalicchio, E., Iannucci, S., & Silvestri, L. (2015). Cloud desktop workload: A characterization study. In Proceedings - 2015 IEEE International Conference on Cloud Engineering, IC2E 2015 (pp.66-75). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/IC2E.2015.25].

Cloud desktop workload: A characterization study

Iannucci S.;
2015

Abstract

Today the cloud-desktop service, or Desktop-as-a-Service (DaaS), is massively replacing Virtual Desktop Infrastructures (VDI), as confirmed by the importance of players entering the DaaS market. In this paper we study the workload of a DaaS provider, analyzing three months of real traffic and resource usage. What emerges from the study, the first on the subject at the best of our knowledge, is that the workload on CPU and disk usage are long-tail distributed (lognormal, weibull and pareto) and that the length of working sessions is exponentially distributed. These results are extremely important for: the selection of the appropriate performance model to be used in capacity planning or run-time resource provisioning; the setup of workload generators; and the definition of heuristic policies for resource provisioning. The paper provides an accurate distribution fitting for all the workload features considered and discusses the implications of results on performance analysis.
978-1-4799-8218-9
Casalicchio, E., Iannucci, S., & Silvestri, L. (2015). Cloud desktop workload: A characterization study. In Proceedings - 2015 IEEE International Conference on Cloud Engineering, IC2E 2015 (pp.66-75). 345 E 47TH ST, NEW YORK, NY 10017 USA : Institute of Electrical and Electronics Engineers Inc. [10.1109/IC2E.2015.25].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/404585
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