On the Feasibility of Prefetching and Caching for Online TV Services: A Measurement Study on Hulu

IEEE/ACM Trans. Networking

Hao

et al. 2015

107

Netflix and Hulu are leading Over-the-Top (OTT) content service providers in the US and Canada. Netflix alone accounts for 29.7% of the peak downstream traffic in the US in 2011. Understanding the system architectures and performance of Netflix and Hulu can shed light on the design of such large-scale video streaming platforms, and help improving the design of future systems. In this paper, we perform extensive measurement study to uncover their architectures and service strategies. Netflix and Hulu bear many similarities. Both Netflix and Hulu video streaming platforms rely heavily on the third-party infrastructures, with Netflix migrating that majority of its functions to the Amazon cloud, while Hulu hosts its services out of Akamai. Both service providers employ the same set of three content distribution networks (CDNs) in delivering the video contents. Using active measurement study, we dissect several key aspects of OTT streaming platforms of Netflix and Hulu, e.g., employed streaming protocols, CDN selection strategy, user experience reporting, etc. We discover that both platforms assign the CDN to a video request without considering the network conditions and optimizing the user-perceived video quality. We further conduct the performance measurement studies of the three CDNs employed by Netflix and Hulu. We show that the available bandwidths on all three CDNs vary significantly over the time and over the geographic locations. We propose a measurement-based adaptive CDN selection strategy and a multiple-CDN-based video delivery strategy that can significantly increase users' average available bandwidth.Index Terms-CDN selection strategy, content distribution networks (CDN), Hulu, Netflix, Over-the-Top (OTT) content service, video streaming.

Section: Related Workmentioning

confidence: 97%

Measurement Study of Netflix, Hulu, and a Tale of Three CDNs

Adhikari

IEEE/ACM Trans. Networking

Hao

et al. 2015

107

“…For example, different user access patterns, session lengths, video popularity, and other content properties have been studied in traditional and live VoD systems [19], [20]. Krishnappa et al examined Hulu traffic, focusing on the potential caching and prefetching at the edge networks [21]. For P2P-assisted streaming systems, there are also lots of studies that aimed to characterize and improve the existing performance [22], [23], [24], [25].…”

Section: Related Workmentioning

confidence: 99%

Investigating Redundant Internet Video Streaming Traffic on iOS Devices: Causes and Solutions

Liu

Wei

et al. 2014

IEEE Trans. Multimedia

Abstract-The Internet has witnessed rapidly increasing streaming traffic to various mobile devices. In this paper, through analysis of a server-side workload and experiments in a controlled lab environment, we find that current practice has introduced a significant amount of redundant traffic. In particular, for the popular iOS based mobile devices, accessing popular Internet streaming services typically involves about 10% -70% redundant traffic. Such a practice not only over-utilizes and wastes resources on the server side and the network (cellular or Internet), but also consumes additional battery power on user's mobile devices and leads to possible monetary cost. To alleviate such a situation without changing the server side or the client side, we design and implement CStreamer that can transparently work between existing mobile clients and servers. We have implemented a prototype and installed on Amazon EC2. Experiments conducted based on this prototype show that CStreamer can completely eliminate the redundant traffic without degrading user's QoS.

“…Yin et al presented the access logs of a live VoD system [21], which shows different user and content properties compared to [17]. Krishnappa et al collected Hulu traffic at a campus edge network, and examined the potential benefits of performing caching and prefetching at edge networks [22]. For P2P-based streaming systems, Wu et al investigated P2P streaming topologies in UUSee [23].…”

Section: Related Workmentioning

confidence: 99%

Measurement and Analysis of an Internet Streaming Service to Mobile Devices

Liu

IEEE Trans. Parallel Distrib. Syst.

et al. 2013

Abstract-ReceivingInternet streaming services on various mobile devices is getting increasingly popular, and cloud platforms have also been gradually employed for delivering streaming services to mobile devices. While a number of studies have been conducted at the client side to understand and characterize Internet mobile streaming delivery, little is known about the server side, particularly for the recent cloud-based Internet mobile streaming delivery. In this work, we aim to investigate the Internet mobile streaming service at the server side. For this purpose, we have collected a four-month server-side log on the cloud (with 1,002 TB delivered video traffic) from a top Internet mobile streaming service provider serving worldwide mobile users. Through trace analysis, we find that (1) a major challenge for providing Internet mobile streaming services is rooted from the mobile device hardware and software heterogeneity. In this workload, we find over 3,400 different hardware models with more than 100 different screen resolutions running 14 different mobile OS and 3 audio codecs and 4 video codecs. (2) To deal with the device heterogeneity, CPU-intensive transcoding is used on the cloud to customize the video to the appropriate versions at runtime for different devices. A video clip could be transcoded into more than 40 different versions in order to serve requests from different devices. (3) Compared to videos in traditional Internet streaming, mobile streaming videos are typically of much smaller size (a median of 1.68 MBytes) and shorter duration (a median of 2.7 minutes). Furthermore, the daily mobile user accesses are more skewed following a Zipf-like distribution but users' interests also quickly shift. Considering the huge demand of CPU cycles for online transcoding, we further examine server-side caching in order to reduce the total CPU cycle demand from the cloud. We show that a policy considering different versions of a video altogether outperforms other intuitive ones when the cache size is limited.