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Stochastic bounding models for performance analysis of clouds

Abstract : We propose to evaluate the performance of a cloud node (data center) using hysteresis queueing systems and stochastic bound methods. We represent the dynamic behavior of the cloud node by a hysteresis queueing system with forward and backward thresholds vectors. The client requests (or jobs) are represented by bulk arrivals arriving into the buffer, and executed by Virtual Machines (VMs) which are activated and deactivated according to the occupation of the queue, and the threshold vectors. As the system is quite difficult to analyze, we propose to define different bounding systems "less complex" and easier to study. Two approaches are used as well, one by aggregating the probability distribution of the batch arrivals and another by taking models with the same sequences of forward and backward threshold. We show the relevance of the proposed bounding system by presenting some numerical results for the performance measures of the data center
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https://hal.archives-ouvertes.fr/hal-01263305
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Wednesday, January 27, 2016 - 4:17:55 PM
Last modification on : Tuesday, March 9, 2021 - 3:27:34 AM

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Farah Ait Salaht, Hind Castel-Taleb. Stochastic bounding models for performance analysis of clouds. CIT 2015 : 15th IEEE International Conference on Computer and Information Technology, Oct 2015, Liverpool, United Kingdom. pp.603 - 610, ⟨10.1109/CIT/IUCC/DASC/PICOM.2015.86⟩. ⟨hal-01263305⟩

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