Cloud Networking


Multi-cloud Network Management

Multi-cloud networks are federations of private network infrastructures from the distinct cloud and third-party providers, and serve as increasingly vital underlays for a range of application domains (e.g., genomics, healthcare, high performance computing). Unfortunately, this emerging connectivity paradigm poses significant management barriers to enterprises that seek to deploy overlays and applications due to providers’ distinct operational practices, privacy concerns, egress costs, among others. This project will investigate a novel measurement-informed learning-based framework called Argus to significantly lower the management barriers faced by modern enterprises.

This project will focus on scientific inquiries in three synergistic thrusts to realize the Argus framework. First, it will design calibrated measurement tools and techniques, using which enterprises can gain unprecedented visibility into the federated underlays. Second, adhering to the privacy concerns of providers, it will investigate learning-based modeling capabilities, using which enterprises can accurately infer, localize, and attribute performance bottlenecks to appropriate providers. Third, it will take a principled approach to design a management capability, using which enterprises can effectively and efficiently navigate egress costs and operational goals while avoiding inferred performance bottlenecks.

Publications

ELF: High-Performance In-band Network Measurement
Joel Sommers and Ramakrishnan Durairajan
In Proceedings of Network Traffic Measurement and Analysis Conference (TMA’21)
Virtual, September 2021.
[PAPER] [CODE]

A First Comparative Characterization of Multi-cloud Connectivity in Today’s Internet
Bahador Yeganeh, Ramakrishnan Durairajan, Reza Rejaie and Walter Willinger
In Proceedings of PAM’20, Oregon, USA, March 2020.
[PAPER]

Team


Multi-cloud Route Characterization

This cloud-centric measurement study examines the performance of three different connectivity options that a coast-to-coast multi-cloud deployment by a typical modern enterprise in the US may adopt.

Today’s enterprises are adopting multi-cloud strategies at an unprecedented pace. Here, a multi-cloud strategy specifies end-to- end connectivity between the multiple cloud providers (CPs) that an enterprise relies on to run its business. This adoption is fueled by the rapid build-out of global-scale private backbones by the large CPs, a rich private peering fabric that interconnects them, and the emergence of new third-party private connectivity providers (e.g., DataPipe, HopOne, etc.). However, little is known about the performance aspects, routing issues, and topological features associated with currently available multi- cloud connectivity options. To shed light on the tradeoffs between these available connectivity options, we take a cloud-to-cloud perspective and present in this study the results of a cloud-centric measurement study of a coast-to-coast multi-cloud deployment that a typical modern enterprise located in the US may adopt. We deploy VMs in two regions (i.e., VA and CA) of each one of three large cloud providers (i.e., AWS, Azure, and GCP) and connect them using three different options: (i) transit provider-based best-effort public Internet (BEP), (ii) third-party provider-based private (TPP) connectivity, and (iii) CP-based private (CPP) connectivity. By performing active measurements in this real- world multi-cloud deployment, we provide new insights into variability in the performance of TPP, the stability in performance and topology of CPP, and the absence of transit providers for CPP.

Cloud Connectivity

Publications

A First Comparative Characterization of Multi-cloud Connectivity in Today’s Internet
Bahador Yeganeh, Ramakrishnan Durairajan, Reza Rejaie and Walter Willinger
In Proceedings of PAM’20, Oregon, USA, March 2020.
[PAPER]

Team

Funding

This material is based upon work supported by the National Science Foundation (NSF) Awards 1838301, 2019170, and NSF CNS 2145813. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.


Unraveling the Cloud Peering Fabric

This study presents a third-party measurement study aimed at revealing all the peerings between Amazon and the rest of the Internet.

The growing demand for an ever-increasing number of cloud ser- vices is profoundly transforming the Internet’s interconnection or peering ecosystem, and one example is the emergence of “virtual private interconnections (VPIs)”. However, due to the underlying technologies, these VPIs are not publicly visible and traffic traversing them remains largely hidden as it bypasses the public Internet. In particular, existing techniques for inferring Internet interconnections are unable to detect these VPIs and are also incapable of mapping them to the physical facility or geographic region where they are established.

In this study, we present a third-party measurement study aimed at revealing all the peerings between Amazon and the rest of the Internet. We describe our technique for inferring these peering links and pay special attention to inferring the VPIs associated with this largest cloud provider. We also present and evaluate a new method for pinning (i.e., geo-locating) each end of the inferred interconnections or peering links. Our study provides a first look at Amazon’s peering fabric. In particular, by grouping Amazon’s peerings based on their key features, we illustrate the specific role that each group plays in how Amazon peers with other networks.

Cloud Traffic

Publications

How Cloud Traffic Goes Hiding: A Study of Amazon’s Peering Fabric
Bahador Yeganeh, Ramakrishnan Durairajan, Reza Rejaie and Walter Willinger
In Proceedings of ACM IMC’19, Amsterdam, Netherlands, October 2019.
[PAPER]

Team

Funding

This material is based upon work supported by the National Science Foundation (NSF) Award 1838301 and 2019170. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF.