Curvature-based Analysis of Network Connectivity in Private Backbone Infrastructures


연구 분야: Cryptography



학회: Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 6, Issue 1


초록

The main premise of this work is that since large cloud providers can and do manipulate probe packets that traverse their privately owned and operated backbones, standard traceroute-based measurement techniques are no longer a reliable means for assessing network connectivity in large cloud provider infrastructures. In response to these developments, we present a new empirical approach for elucidating private connectivity in today's Internet. Our approach relies on using only "light-weight" ( i.e., simple, easily-interpretable, and readily available) measurements, but requires applying a "heavy-weight" or advanced mathematical analysis. In particular, we describe a new method for assessing the characteristics of network path connectivity that is based on concepts from Riemannian geometry ( i.e., Ricci curvature) and also relies on an array of carefully crafted visualizations ( e.g., a novel manifold view of a network's delay space). We demonstrate our method by utilizing latency measurements from RIPE Atlas anchors and virtual machines running in data centers of three large cloud providers to (i) study different aspects of connectivity in their private backbones and (ii) show how our manifold-based view enables us to expose and visualize critical aspects of this connectivity over different geographic scales.


Author Profile
Loqman Salamatian

Columbia University New York NY USA

United States
Author Profile
Mark Edward Crovella

Boston University Boston MA USA

Morocco
Author Profile
Scott Anderson

University of Wisconsin Madison Madison WI USA

United States

📄 논문 정보

발행 연도 2022년
인용수 2
출판 국가 Morocco, United States
사이트 ACM
좋아요 수 0

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