1,720,973 research outputs found
Measuring network throughput in the cloud: The case of Amazon EC2
No full textCloud providers employ sophisticated virtualization techniques and strategies for sharing resources among a large number of largely uncoordinated and mutually untrusted customers. The shared networking environment, in particular, dictates the need for mechanisms to partition network resources among virtual machines. At the same time, the performance of applications deployed over these virtual machines may be heavily impacted by the performance of the underlying network, and therefore by such mechanisms. Nevertheless, due to security and commercial reasons, providers rarely provide detailed information on network organization, performance, and mechanisms employed to regulate it. In addition, the scientific literature only provides a blurred image of the network performance inside the cloud. The few available pioneer works marginally focus on this aspect, use different methodologies, operate in few limited scenarios, or report conflicting results.
In this paper, we present a detailed analysis of the performance of the internal network of Amazon EC2, performed by adopting a non-cooperative experimental evaluation approach (i.e. not relying on provider support). Our aim is to provide a quantitative assessment of the networking performance as a function of the several variables available, such as geographic region, resource price or size. We propose a detailed methodology to perform this kind of analysis, which we believe is essential in a such complex and dynamic environment. During this analysis we have discovered and analyzed the limitations enforced by Amazon over customer traffic in terms of maximum throughput allowed. Thanks to our work it is possible to understand how the complex mechanisms enforced by the provider in order to manage its infrastructure impact the performance perceived by the cloud customers and potentially tamper with monitoring and controlling approaches previously proposed in literature. Leveraging our knowledge of the bandwidth-limiting mechanisms, we then present a clear picture of the maximum throughput achievable in Amazon EC2 network, shedding light on when and how such maximum throughput can be achieved and at which cost
On the performance of the wide-area networks interconnecting public-cloud datacenters around the globe
No full textAccording to current usage patterns, research trends, and latest reports, the performance of the wide-area networks interconnecting geographically distributed cloud nodes (i.e. inter-datacenter networks) is gaining more and more interest. In this paper we leverage only active approaches—thus we do not rely on information restricted to providers—and propose a deep analysis of these infrastructures for the two public-cloud leading providers: Amazon Web Services and Microsoft Azure. Our study provides an assessment of the performance of these networks as a function of the several configuration factors under the control of the customer and evidences specific cases of particular interest. The analysis of these cases and of their root causes, also related with service fees, provides insights on their impact on both the Quality of Service perceived by cloud customers and the outcomes of studies neglecting them.
Our results show that Azure inter-datacenter infrastructure performs better than Amazon’s in terms of throughput (+56% on average). On the other hand, the performance of the two providers is comparable in terms of latency, with the exception of limited specific cases. Moreover, some of the configuration factors cloud customers can leverage (such as larger more expensive VM sizes, advertised to have better network performance) may have no effect on the inter-datacenter network performance actually perceived. Counterintuitively, lower performance may even be related to higher costs for the customer. Experimental evidences show that public-cloud providers also rely on external network providers for some geographical regions, which is the cause of lower performance and higher costs. A comparison with previous works show that TCP throughput has not been improved recently, while evidences of higher link capacities have been found
On Network Throughput Variability in Microsoft Azure Cloud
No full textThe 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
Measuring Networks Using IP Options
No full textInjecting purposely-created probing traffic makes it possible to detect the presence and location of performance issues or faults, to reveal the topology of the network, and to investigate related properties. While researchers and network operators still rely on traditional tools (e.g., traceroute or ping) to shed light on the Internet, here we present six novel measurement techniques based on IP options. We show how IP options can still offer unforeseen ways to augment the knowledge about networks, potentially presenting both new threats and new opportunities for different stakeholders
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