Watching user generated videos with prefetching

Khemmarat, Samamon; Zhou, Renjie; Gao, Lixin; Zink, Michael

doi:10.1145/1943552.1943578

Cited by 30 publications

(17 citation statements)

References 18 publications

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“…There are also several techniques proposed to adjust QoS parameters at the client, at the server, or in the network [19]. Those techniques include bitrate adaptation [20], prefetching [21,22], transport protocol selection [14], and cache deployment [23].…”

Section: Qos Parametersmentioning

confidence: 99%

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

Tan

Guo

Orgun

et al. 2018

Wireless Communications and Mobile Computing

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With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

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Section: Qos Parametersmentioning

confidence: 99%

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

Tan

Guo

Orgun

et al. 2018

Wireless Communications and Mobile Computing

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“…Khemmarat et al (2012) stated that the recommendations generated through the prefetching mechanism could reach a HR of up to 81%, but the HR without prefetching only reached 40%. In contrast, studies by Agustin (2015) showed there was an increase in CPU overhead of up to 12% when latency had decreased.…”

Section: Caching Strategymentioning

confidence: 99%

“…However, strict prediction rules can reduce the benefits because there will not be much content that downloaded into the cache server (Teng et al , 2005). Predictive mechanisms that are too aggressive will actually use more bandwidth and cause network congestion to reduce user experience (Deng and Manoharan, 2015; Khemmarat et al , 2012). Therefore, this prediction mechanism must be continuously evaluated to avoid prediction results never used, which impact system performance degradation because of wasted cache capacity (Deng and Manoharan, 2017).…”

Section: Caching Strategymentioning

confidence: 99%

Caching strategy for Web application – a systematic literature review

Zulfa

Hartanto

Permanasari

2020

IJWIS

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Purpose Internet users and Web-based applications continue to grow every day. The response time on a Web application really determines the convenience of its users. Caching Web content is one strategy that can be used to speed up response time. This strategy is divided into three main techniques, namely, Web caching, Web prefetching and application-level caching. The purpose of this paper is to put forward a literature review of caching strategy research that can be used in Web-based applications. Design/methodology/approach The methods used in this paper were as follows: determined the review method, conducted a review process, pros and cons analysis and explained conclusions. The review method is carried out by searching literature from leading journals and conferences. The first search process starts by determining keywords related to caching strategies. To limit the latest literature in accordance with current developments in website technology, search results are limited to the past 10 years, in English only and related to computer science only. Findings Note in advance that Web caching and Web prefetching are slightly overlapping techniques because they have the same goal of reducing latency on the user’s side. But actually, the two techniques are motivated by different basic mechanisms. Web caching uses the basic mechanism of cache replacement or the algorithm to change cache objects in memory when the cache capacity is full, whereas Web prefetching uses the basic mechanism of predicting cache objects that can be accessed in the future. This paper also contributes practical guidelines for choosing the appropriate caching strategy for Web-based applications. Originality/value This paper conducts a state-of-the art review of caching strategies that can be used in Web applications. Exclusively, this paper presents taxonomy, pros and cons of selected research and discusses data sets that are often used in caching strategy research. This paper also provides another contribution, namely, practical instructions for Web developers to decide the caching strategy.

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“…Videos with a medium popularity seem to have longer session times. An advanced caching and prefetching policy [28] should utilize this difference to be able to improve the QoS while reducing wasted resources, e.g., by caching the first 16 to 20 minutes for videos with medium popularity, streaming the first 3 minutes for videos with low popularity and caching and prefetching the first 10 minutes for videos with high popularity.…”

Section: Cachingmentioning

confidence: 99%

“…Khemmarat et al [28] collect user browsing pattern data for YouTube. They show that video buffering affects the QoS for YouTube users due to disruptions for buffering.…”

Section: Median Session Lengthmentioning

confidence: 99%

Analysis and characterization of a video-on-demand service workload

Ali-Eldin

Kihl

Tordsson

et al. 2015

Proceedings of the 6th ACM Multimedia Systems Conference

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Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload and their cause, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Contrary to some previously analyzed workloads in the literature, our results show that the user and the session arrival rates for the TV4 workload does not follow a Poisson process. The arrival rate distribution is modeled using a lognormal distribution while the interarrival time distribution is modeled using a stretched exponential distribution. We observe the "impatient user" behavior where users abandon streaming sessions after minutes or even seconds of starting them. Both very popular videos and non-popular videos are specially affected by impatient users. We also show that this behavior is an invariant in VoD workloads and is neither affected by the average bit-rate nor by the number of videos a user watch.

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Watching user generated videos with prefetching

Cited by 30 publications

References 18 publications

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

An Engagement Model Based on User Interest and QoS in Video Streaming Systems

Caching strategy for Web application – a systematic literature review

Analysis and characterization of a video-on-demand service workload

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