The dependence of the industry on cloud-based infrastructures has grown much faster than our understanding of the performance limits and dynamics of these environments. An aspect only marginally analyzed in the past is related to the performance of the intra-cloud network connecting the virtual machines (VMs) deployed in the same data center. The few available works either do not exhaustively describe the adopted methodology or employed different approaches causing the analyses to be hard to replicate, and the results to be hard to compare. In addition, cloud customers can today highly customize their cloud environments while previous works considered only a few of the scenarios in which a customer may operate. In this paper, we provide an intra-cloud network performance characterization of Microsoft (MS) Azure, a leading provider only preliminary investigated from this angle. We first propose and thoroughly detail a methodology to carry out similar analyses, thus encouraging its replication also in other contexts; then we apply this methodology to characterize the intra-cloud network performance in terms of maximum network throughput. More specifically, we investigate whether and how the achievable throughput between two VMs varies (i) over time; (ii) when the customer operates different decisions on VM size, network configuration, geographic region, and transport protocol; and (iii) when the customer operates the same decisions on these factors. Our analysis aims at addressing the gap existing in the literature by providing the most exhaustive and detailed results about the intra-cloud network performance for MS Azure today available.

On Network Throughput Variability in Microsoft Azure Cloud / Persico, Valerio; Marchetta, Pietro; Botta, Alessio; Pescape, Antonio. - (2015), pp. 1-6. (Intervento presentato al convegno 2015 IEEE Global Communications Conference (GLOBECOM) tenutosi a San Diego, CA, USA) [10.1109/GLOCOM.2015.7416997].

On Network Throughput Variability in Microsoft Azure Cloud

Persico, Valerio;Marchetta, Pietro;Botta, Alessio;Pescape, Antonio
2015

Abstract

The dependence of the industry on cloud-based infrastructures has grown much faster than our understanding of the performance limits and dynamics of these environments. An aspect only marginally analyzed in the past is related to the performance of the intra-cloud network connecting the virtual machines (VMs) deployed in the same data center. The few available works either do not exhaustively describe the adopted methodology or employed different approaches causing the analyses to be hard to replicate, and the results to be hard to compare. In addition, cloud customers can today highly customize their cloud environments while previous works considered only a few of the scenarios in which a customer may operate. In this paper, we provide an intra-cloud network performance characterization of Microsoft (MS) Azure, a leading provider only preliminary investigated from this angle. We first propose and thoroughly detail a methodology to carry out similar analyses, thus encouraging its replication also in other contexts; then we apply this methodology to characterize the intra-cloud network performance in terms of maximum network throughput. More specifically, we investigate whether and how the achievable throughput between two VMs varies (i) over time; (ii) when the customer operates different decisions on VM size, network configuration, geographic region, and transport protocol; and (iii) when the customer operates the same decisions on these factors. Our analysis aims at addressing the gap existing in the literature by providing the most exhaustive and detailed results about the intra-cloud network performance for MS Azure today available.
2015
978-1-4799-5952-5
On Network Throughput Variability in Microsoft Azure Cloud / Persico, Valerio; Marchetta, Pietro; Botta, Alessio; Pescape, Antonio. - (2015), pp. 1-6. (Intervento presentato al convegno 2015 IEEE Global Communications Conference (GLOBECOM) tenutosi a San Diego, CA, USA) [10.1109/GLOCOM.2015.7416997].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/718739
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