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Bandwidth Measurements within the Cloud: Characterizing Regular Behaviors and Correlating Downtimes

机译:云中的带宽测量:表征定期行为和关联下降时间

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The search for availability, reliability, and quality of service has led cloud infrastructure customers to disseminate their services, contents, and data over multiple cloud data centers, often involving several Cloud service providers (CSPs). The consequence of this is that a large amount of data must be transmitted across the public Cloud. However, little is known about the bandwidth dynamics involved. To address this, we have conducted a measurement campaign for bandwidth between 18 data centers of four major CSPs. This extensive campaign allowed us to characterize the resulting time series of bandwidth as the addition of a stationary component and some infrequent excursions (typically downtimes). While the former provides a description of the bandwidth users can expect in the Cloud, the latter is closely related to the robustness of the Cloud (i.e., the occurrence of downtimes is correlated). Both components have been studied further by applying factor analysis, specifically analysis of variance, as a mechanism to formally compare data centers’ behaviors and extract generalities. The results show that the stationary process is closely related to the data center locations and CSPs involved in transfers that, fortunately, make the Cloud more predictable and allow the set of reported measurements to be extrapolated. On the other hand, although correlation in the Cloud is low, that is, only 10% of the measured pair of paths showed some correlation, we found evidence that such correlation depends on the particular relationships between pairs of data centers with little connection to more general factors. Positively, this implies that data centers either in the same area or within the same CSP do not show qualitatively more correlation than other data centers, which eases the deployment of robust infrastructures. On the downside, this metric is scarcely generalizable and, consequently, calls for exhaustive monitoring.
机译:搜索可用性,可靠性和服务质量的LED云基础架构客户通过多个云数据中心传播其服务,内容和数据,通常涉及几个云服务提供商(CSP)。结果是必须在公共云中传输大量数据。但是,关于所涉及的带宽动态知之甚少。要解决此问题,我们在四个主要CSP的18个数据中心之间进行了测量运动。这一广泛的广告系列使我们可以将带宽的带宽序列表征为添加静止组件和一些不常见的短途旅行(通常是下降时间)。虽然前者提供带宽用户的描述可以期望在云中,但后者与云的鲁棒性密切相关(即,停机时间相关的发生)。通过应用因子分析,特别是对方差分析,作为正式比较数据中心行为和提取总体的机制,进一步研究了两种组分。结果表明,静止过程与传输中涉及的数据中心位置和CSP密切相关,幸运的是,使云更能可预测并允许预包装的报告的测量集。另一方面,虽然云中的相关性低,即,只有10%的测量路径显示了一些相关性,但我们发现这些相关性取决于数据中心对与更多的数据中心之间的特定关系相比一般因素。积极地,这意味着数据中心在同一区域或同一CSP中没有比其他数据中心的定性更多地显示,这缓解了强大的基础架构的部署。在缺点方面,这种指标几乎不够宽大,因此要求彻底监测。

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