Back-Office Web Traffic on The Internet

Pujol, Enric; Richter, Philipp; Chandrasekaran, B.; Smaragdakis, Georgios; Feldmann, Anja; Maggs, Bruce M.; Ng, Keung-Chi

doi:10.1145/2663716.2663756

Cited by 21 publications

(10 citation statements)

References 59 publications

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“…Large companies that generate and deliver content like Google [24,32,77], Facebook [65], Microsoft [10], Amazon [11], and Netix [50] created their own CDNs. This approach allows them to customize and to optimize content delivery for their needs and assets [36,45,60,68]. Given the scale at which they operate, it can also reduce their costs.…”

Section: Introductionmentioning

confidence: 99%

Steering hyper-giants' traffic at scale

Pujol¹,

Poese²,

Zerwas³

et al. 2019

Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

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Large content providers, known as hyper-giants, are responsible for sending the majority of the content trac to consumers. These hyper-giants operate highly distributed infrastructures to cope with the ever-increasing demand for online content. To achieve commercial-grade performance of Web applications, enhanced enduser experience, improved reliability, and scaled network capacity, hyper-giants are increasingly interconnecting with eyeball networks at multiple locations. This poses new challenges for both (1) the eyeball networks having to perform complex inbound trac engineering, and (2) hyper-giants having to map end-user requests to appropriate servers. We report on our multi-year experience in designing, building, rolling-out, and operating the rst-ever large scale system, the Flow Director, which enables automated cooperation between one of the largest eyeball networks and a leading hyper-giant. We use empirical data collected at the eyeball network to evaluate its impact over two years of operation. We nd very high compliance of the hyper-giant to the Flow Director's recommendations, resulting in (1) close to optimal user-server mapping, and (2) 15% reduction of the hyper-giant's trac overhead on the ISP's long-haul links, i.e., benets for both parties and end-users alike.

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Section: Introductionmentioning

confidence: 99%

Steering hyper-giants' traffic at scale

Pujol¹,

Poese²,

Zerwas³

et al. 2019

Proceedings of the 15th International Conference on Emerging Networking Experiments and Technologies

Self Cite

View full text Add to dashboard Cite

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“…This can be a blessing if these installations can improve end user performance. For example, it has been reported that round trip times between end-users and these servers can be even less than a millisecond [86,104]. Such low delays are a step towards addressing the future requirements and a way for Hypergiants like Google and Facebook to play an important role in future services, including 5G.…”

Section: Discussionmentioning

confidence: 99%

“…The extended serving infrastructure can also better deal with increased traffic, e.g., during the COVID-19 pandemic [43], as was reported recently by Google [56] and Akamai [70]. At the same time, these deployments mean that a substantial fraction of traffic demand can be served locally without crossing inter-domain links or private interconnections [81,86,90,111,113,114]. This "zero AS-hop" Internet has regulatory implications.…”

Section: Discussionmentioning

confidence: 99%

Seven years in the life of Hypergiants' off-nets

Gigis

Calder

Manassakis

et al. 2021

Proceedings of the 2021 ACM SIGCOMM 2021 Conference

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Content Hypergiants deliver the vast majority of Internet traffic to end users. In recent years, some have invested heavily in deploying services and servers inside end-user networks. With several dozen Hypergiants and thousands of servers deployed inside networks, these off-net (meaning outside the Hypergiant networks) deployments change the structure of the Internet. Previous efforts to study them have relied on proprietary data or specialized per-Hypergiant measurement techniques that neither scale nor generalize, providing a limited view of content delivery on today's Internet.In this paper, we develop a generic and easy to implement methodology to measure the expansion of Hypergiants' off-nets. Our key observation is that Hypergiants increasingly encrypt their traffic to protect their customers' privacy. Thus, we can analyze publicly available Internet-wide scans of port 443 and retrieve TLS certificates to discover which IP addresses host Hypergiant certificates in order to infer the networks hosting off-nets for the corresponding Hypergiants. Our results show that the number of networks hosting Hypergiant off-nets has tripled from 2013 to 2021, reaching 4.5k networks. The largest Hypergiants dominate these deployments, with almost all of these networks hosting an off-net for at least one -and increasingly two or more -of Google, Netflix, Facebook, or Akamai. These four Hypergiants have off-nets within networks that provide access to a significant fraction of end user population.

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“…Herein, we call this address the client-facing IP, and the other address we call the operations IP. The operations IP is used solely for CDN-internal communication [29]. The distinction between these two addresses that is relevant for this study is that the operations IP is never exposed in replies to forward DNS queries.…”

Section: Data Collection and Samplingmentioning

confidence: 99%

Scanning the Scanners

Richter

Berger

2019

Proceedings of the Internet Measurement Conference

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Scanning of hosts on the Internet to identify vulnerable devices and services is a key component in many of today's cyberattacks. Tracking this scanning activity, in turn, provides an excellent signal to assess the current state-of-affairs for many vulnerabilities and their exploitation. So far, studies tracking scanning activity have relied on unsolicited traffic captured in darknets, focusing on random scans of the address space. In this work, we track scanning activity through the lens of unsolicited traffic captured at the firewalls of some 89,000 hosts of a major Content Distribution Network (CDN). Our vantage point has two distinguishing features compared to darknets: (i) it is distributed across some 1,300 networks, and (ii) its servers are live, offering services and thus emitting traffic. While all servers receive a baseline level of probing from Internet-wide scans, i.e., scans targeting random subsets of or the entire IPv4 space, we show that some 30% of all logged scan traffic is the result of localized scans. We find that localized scanning campaigns often target narrow regions in the address space, and that their characteristics in terms of target selection strategy and scanned services differ vastly from the more widely known Internet-wide scans. Our observations imply that conventional darknets can only partially illuminate scanning activity, and may severely underestimate widespread attempts to scan and exploit individual services in specific prefixes or networks. Our methods can be adapted for individual network operators to assess if they are subjected to targeted scanning activity.

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Back-Office Web Traffic on The Internet

Cited by 21 publications

References 59 publications

Steering hyper-giants' traffic at scale

Steering hyper-giants' traffic at scale

Seven years in the life of Hypergiants' off-nets

Scanning the Scanners

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