Public clouds fundamentally changed the Internet landscape, centralizing traffic generation in a handful of networks. Internet performance, robustness, and public policy analyses struggle to properly reflect this centralization, largely because public collections of BGP and traceroute reveal a small portion of cloud connectivity. This paper evaluates and improves our ability to infer cloud connectivity, bootstrapping future measurements and analyses that more accurately reflect the cloud-centric Internet. We also provide a technique for identifying the interconnections that clouds use to reach destinations around the world, allowing edge networks and enterprises to understand how clouds reach them via their public WAN. Finally, we present two techniques for geolocating the interconnections between cloud networks at the city level that can inform assessments of their resilience to link failures and help enterprises build multi-cloud applications and services. 
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                            PAINTER: Ingress Traffic Engineering and Routing for Enterprise Cloud Networks
                        
                    
    
            Enterprises increasingly use public cloud services for critical business needs. However, Internet protocols force clouds to contend with a lack of control, reducing the speed at which clouds can respond to network problems, the range of solutions they can provide, and deployment resilience. To overcome this limitation, we present PAINTER, a system that takes control over which ingress routes are available and which are chosen to the cloud by leveraging edge proxies. PAINTER efficiently advertises BGP prefixes, exposing more concurrent routes than existing solutions to improve latency and resilience. Compared to existing solutions, PAINTER reduces path inflation by 75% while using a third of the prefixes of other solutions, avoids 20% more path failures, and chooses ingresses from the edge at finer time (RTT) and traffic (per-flow) granularities, enhancing our agility. 
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                            - Award ID(s):
- 2323307
- PAR ID:
- 10540280
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400702365
- Format(s):
- Medium: X
- Location:
- New York NY USA
- Sponsoring Org:
- National Science Foundation
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