Analyzing video services in Web 2.0

Saxena, Mohit; Sharan, Umang; Fahmy, Sonia

doi:10.1145/1496046.1496056

Cited by 65 publications

(41 citation statements)

References 10 publications

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“…They analyzed YouTube video distribution architecture, and found that YouTube deploys a large number of video caching server, that vary in size and geographical locations, in order to reduce cost and improve the end-user performance. Saxena et al [16] analyzed how three video content providers (YouTube, Dailymotion and Metacafe) distribute their video streaming services in terms of clients' geographical locations and video characteristics such as age and popularity. Our approach differs from the prior work in two aspects: a) Noticeably, in some cases, we found that YouTube assigns a non-optimal video content server to a client; b) Unlike ALTO [7], our proposed solutions do not require any additional implementation neither at the server nor at the client.…”

Section: Discussionmentioning

confidence: 99%

Towards dynamic network condition-aware video server selection algorithms over wireless networks

Nam

Kim

Calin³

et al. 2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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Abstract-We investigate video server selection algorithms in a distributed video-on-demand system. We conduct a detailed study of the YouTube Content Delivery Network (CDN) on PCs and mobile devices over Wi-Fi and 3G networks under varying network conditions. We proved that a location-aware video server selection algorithm assigns a video content server based on the network attachment point of a client. We found out that such distance-based algorithms carry the risk of directing a client to a less optimal content server, although there may exist other better performing video delivery servers. In order to solve this problem, we propose to use dynamic network information such as packet loss rates and Round Trip Time (RTT) between an edge node of an wireless network (e.g., an Internet Service Provider (ISP) router in a Wi-Fi network and a Radio Network Controller (RNC) node in a 3G network) and video content servers, to find the optimal video content server when a video is requested. Our empirical study shows that the proposed architecture can provide higher TCP performance, leading to better viewing quality compared to location-based video server selection algorithms.

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Section: Discussionmentioning

confidence: 99%

Towards dynamic network condition-aware video server selection algorithms over wireless networks

Nam

Kim

Calin³

et al. 2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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“…Additionally, in [10] there is an analytical comparison among YouTube and other video sharing services via crawling the websites and measuring delays. In [11], there is a comparison between PC and mobile users of YouTube and how their behavior can be related to system performance degradation.…”

Section: B Infrastructurementioning

confidence: 99%

Analysis of YouTube user experience from passive measurements

Dimopoulos

Barlet‐Ros

Sanjuàs-Cuxart

2013

Proceedings of the 9th International Conference on Network and Service Management (CNSM 2013)

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Abstract-In this paper, we analyze the YouTube service and the traffic generated from its usage. The purpose of this study is to identify by strictly using passive measurements the information that can be used as metrics or indicators of the progress of individual video sessions and to estimate the impact of these metrics in the user experience. We find a novel method to track the progress of the video playback that, in contrast to previous works, does not require instrumentation of the video player neither browser-based plug-ins. Instead, we extract important statistical information about the status of the playback by reverse engineering the metrics in related HTTP requests that are generated during playback. For the purpose of collecting these metrics, a tool was developed to perform YouTube traffic measurements by means of passive network monitoring in a large university campus network. The analysis of the obtained data revealed the most important sources of initial delay in the sessions as well as buffer outage events and download rate statistics. Further analysis revealed the impact of video advertisements and re-buffering events on the user experience in terms of video abandonment rate.

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“…However, they do not study a deeply distributed CDN like Akamai which handles dynamic content. Several studies have investigated data center performance [10], [20]. The WhyHigh tool [10] diagnoses high latency to Google's data centers and finds causes related to inter-domain routing, however effective solutions are not proposed.…”

Section: Related Workmentioning

confidence: 99%

Content retrieval using cloud-based DNS

Khosla

Fahmy

2012

2012 Proceedings IEEE INFOCOM Workshops

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Abstract-Cloud-computing systems are rapidly gaining momentum, providing flexible alternatives to many services. We study the Domain Name System (DNS) service, used to convert host names to IP addresses, which has historically been provided by a client's Internet Service Provider (ISP). With the advent of cloud-based DNS providers such as Google and OpenDNS, clients are increasingly using these DNS systems for URL and other name resolution.Performance degradation with cloud-based DNS has been reported, especially when accessing content hosted on highly distributed CDNs like Akamai. In this work, we investigate this problem in depth using Akamai as the content provider and Google DNS as the cloud-based DNS system. We demonstrate that the problem is rooted in the disparity between the number and location of servers of the two providers, and develop a new technique for geolocating data centers of cloud providers. Additionally, we explore the design space of methods for cloudbased DNS systems to be effective. Client-side, cloud-side, and hybrid approaches are presented and compared, with the goal of achieving the best client-perceived performance. Our work yields valuable insight into Akamai's DNS system, revealing previously unknown features.

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Analyzing video services in Web 2.0

Cited by 65 publications

References 10 publications

Towards dynamic network condition-aware video server selection algorithms over wireless networks

Towards dynamic network condition-aware video server selection algorithms over wireless networks

Analysis of YouTube user experience from passive measurements

Content retrieval using cloud-based DNS

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