Revealing the Evolution of a Cloud Provider Through its Network Weather Map

peer reviewedResearchers often face the lack of data on large operational networks to understand how they are used, how they behave, and sometimes how they fail. This data is crucial to drive the evolution of Internet protocols and develop techniques such as traffic engineering, DDoS detection and mitigation. Companies that have access to measurements from operational networks and services leverage this data to improve the availability, speed, and resilience of their Internet services. Unfortunately, the availability of large datasets, especially collected regularly over a long period of time, is a daunting task that remains scarce in the literature. We tackle this problem by releasing a dataset collected over roughly two years of observations of a major cloud company (OVH). Our dataset, called OVH Weather dataset, represents the evolution of more than 180 routers, 1,100 internal links, 500 external links, and their load percentages in the backbone network over time. Our dataset has a high density with snapshots taken every five minutes, totaling more than 500,000 files. In this paper, we also illustrate how our dataset could be used to study the backbone networks evolution. Finally, our dataset opens several exciting research questions that we make available to the research community

Controlled synthesis of traffic matrices

Date of publication January 24, 2017; date of current version June 14, 2017.The traffic matrix (TM) is a chief input in many network design and planning applications. In this paper, we propose a model, called the spherically additive noise model (SANM). In conjunction with iterative proportional fitting (IPF), it enables fast generation of synthetic TMs around a predicted TM. We analyze SANM and IPF’s action on the model to show theoretical guarantees on asymptotic convergence, in particular, convergence to the well-known gravity model.Paul Tune and Matthew Rougha