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

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.