Demystifying commercial content delivery networks in China

Yin, Hao; Qiao, Bo; Luo, Yan; Tian, Chen; Yang, Yufan

doi:10.1002/cpe.3464

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…That is, the elaborated sampling bias should not be expected to tend to infinity. While speculating about the actual upper bounds is beyond the scope of this paper, it can be noted that the address pools may not be overly large in current CDNs [30]. Fast flux networks are a different beast.…”

Section: The Agility Biasmentioning

confidence: 99%

Investigating the Agility Bias in DNS Graph Mining

Ruohonen¹,

Leppänen²

2017

2017 IEEE International Conference on Computer and Information Technology (CIT)

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The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based graph theoretical data mining applications. By building on two conventional metrics for observing malicious DNS agility, the agility bias is observed by comparing bipartite DNS graphs to different subgraphs from which vertices and edges are removed according to two criteria. According to an empirical experiment with two longitudinal DNS datasets, irrespective of the criterion, the agility bias is observed to be severe particularly regarding the effect of outlying domains hosted and delivered via content delivery networks and cloud computing services. With these observations, the paper contributes to the research domains of cyber security and DNS mining. In a larger context of applied graph mining, the paper further elaborates the practical concerns related to the learning of large and dynamic bipartite graphs.

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Section: The Agility Biasmentioning

confidence: 99%

Investigating the Agility Bias in DNS Graph Mining

Ruohonen¹,

Leppänen²

2017

2017 IEEE International Conference on Computer and Information Technology (CIT)

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“…Thoroughly understanding the CDN industry from different aspects including market choice, technology, performance, tendency and infrastructure is indispensable to future Internet. Comprehensive studies on challenges to the existing commercial CDNs are provided [34]. Different CDN architectures have different relative strengths and weaknesses.…”

Section: A Content Delivery Networkmentioning

confidence: 99%

Content Delivery Networks in Cross-border e-Commerce

Strzelecki¹

2020

IJACSA

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Cross-border e-commerce has been growing in recent years. Purchasing goods from abroad is getting easier due to global deliveries, well-known payment methods and decreasing language barriers. Content Delivery Network (CDN) is a technical solution. Deploying CDN for cross-border e-commerce can improve performance and consumer experience. In this paper, four sets of cross-border online stores, containing together 57 e-commerce stores, are examined. Each set is a group of online stores operating in many European markets with different Domain Name System (DNS) settings. Two sets use Cloudflare CDN and its DNS server. Other two sets use country DNS server settings without CDN. Results show that DNS lookup time significantly decrease for cross-border users when online stores are using a CDN, improving overall website time load. Increase of speed for resolving domain names is on average from 40ms to 5ms. No significant improvement was observed for user in the same country location.

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“…With era of big data and development of web data mining , increasing number of scholars are interested in web‐based events mining . Traditional events discovery methods often cluster similar documents together , including single‐pass clustering methods .…”

Section: Related Workmentioning

confidence: 99%

Event space model in virtual and real society based on special field

Xue

et al. 2016

Concurrency and Computation

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Summary With the development and popularization of Internet big data, the cyber world nearly parallels to the real world to reflect social hot event. However, the multi‐source and heterogeneity features of reality data lead to incompleteness and unreality based on Internet data. To solve the above issues, we propose Event Space Model (ESM) for event analysis by multi‐viewer. In the model, each event is mapped into Network Public Opinion Data Space (OS) and Actual Behavior Data Space (BS). In addition, we propose an Event Starting Point Discovery Algorithm (ESPDA) to effectively detect starting point of event and to identify the representation of event trajectory in OS and BS. The experimental results prove that the ESM represent event from multi‐viewer, while the ESPDA intensively identify events and improve recall and precision of starting point of event. Copyright © 2016 John Wiley & Sons, Ltd.

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Demystifying commercial content delivery networks in China

Cited by 8 publications

References 16 publications

Investigating the Agility Bias in DNS Graph Mining

Investigating the Agility Bias in DNS Graph Mining

Content Delivery Networks in Cross-border e-Commerce

Event space model in virtual and real society based on special field

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