On the merits of popularity prediction in multimedia content caching

Famaey, Jeroen; Wauters, Tim; Turck, Filip De

doi:10.1109/inm.2011.5990669

Cited by 20 publications

(11 citation statements)

References 19 publications

(19 reference statements)

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“…This article extends previous work by ourselves [30], where the theoretical variant of the predictive cache replacement strategy was presented. Here, a practical prediction algorithm is proposed and combined with the previously introduced strategy.…”

Section: Related Worksupporting

confidence: 76%

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Towards a predictive cache replacement strategy for multimedia content

Famaey

Iterbeke

Wauters

et al. 2013

Journal of Network and Computer Applications

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In recent years, telecom operators have been moving away from traditional broadcast-driven television, towards IP-based interactive and on-demand multimedia services. Consequently, multicast is no longer sufficient to limit the amount of generated traffic in the network. In order to prevent an explosive growth in traffic, caches can be strategically placed throughout the content delivery infrastructure. As the size of caches is usually limited to only a small fraction of the total size of all content items, it is important to accurately predict future content popularity. Traditional caching strategies only take into account the past when deciding what content to cache. Recently, a trend towards novel strategies that actually try to predict future content popularity has arisen. In this article, we ascertain the viability of using popularity prediction in realistic multimedia content caching scenarios. The proposed generic popularity prediction algorithm is capable of predicting future content popularity, independent of specific content and service characteristics. Additionally, a novel cache replacement strategy, which employs the popularity prediction algorithm when making its decisions, is introduced. A detailed evaluation, based on simulation results using trace files from an actual deployed Video on Demand service, was performed. The evaluation results are used to determine the merits of popularity-based caching compared to traditional strategies. Additionally, the synergy between several parameters, such as cache size and prediction window, is investigated. Results show that the proposed prediction-based caching strategy has the potential to significantly outperform state-of-the-art traditional strategies. Specifically, the evaluated Video on Demand scenario showed a performance increase of up to 20% in terms of cache hit rate.

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Section: Related Worksupporting

confidence: 76%

“…Throughout this evaluation a history window of 12 hours is used for LFU. We have previously shown this to be a near optimal value for small cache sizes in combination with the dataset used in this article [30]. The cache hit rate is used as an evaluation metric.…”

Section: Resultsmentioning

confidence: 89%

Towards a predictive cache replacement strategy for multimedia content

Famaey

Iterbeke

Wauters

et al. 2013

Journal of Network and Computer Applications

Self Cite

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“…Different from the user-generated content (UGC) and movies, online serials often have many regularly updated episodes, which will continuously capture the audiences' preference. Indeed, an interesting and practical problem for this online serial service is to predict the popularity of online serials, which can help with a wide range of applications, such as serial recommendation [21,29], online advertising [22,14,29,1,23], user behavior analysis [1,13,8,32], as well as online traffic management [7,2,9].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Popularity of Online Serials with Autoregressive Models

Chang

Zhu

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Recent years have witnessed the rapid prevalence of online serials, which play an important role in our daily entertainment. A critical demand along this line is to predict the popularity of online serials, which can enable a wide range of applications, such as online advertising, and serial recommendation. However, compared with traditional online media such as user-generated content (UGC), online serials have unique characteristics of sequence dependence, release date dependence as well as unsynchronized update regularity. Therefore, the popularity prediction for online serials is a nontrivial task and still under-addressed. To this end, in this paper we present a comprehensive study for predicting the popularity of online serials with autoregressive models. Specifically, we first introduce a straightforward yet effective Naive Autoregressive (NAR) model based on the correlations of serial episodes. Furthermore, we develop a sophisticated model, namely Transfer Autoregressive (TAR) model, to capture the dynamic behaviors of audiences, which can achieve better prediction performance than the NAR model. Indeed, the two models can reveal the popularity generation from different perspectives. In addition, as a derivative of the TAR model, we also design a novel metric, namely favor, for evaluating the quality of online serials. Finally, extensive experiments on two real-world data sets clearly show that both models are effective and outperform baselines in terms of the popularity prediction for online serials. And the new metric performs better than other metrics for quality estimation.

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“…These so-called user-generated contents range from the breaking news to one's daily trivia. Recently, the study of trendy user-generated contents and their popularity prediction has shown interests among researchers, as it can be applied to many fields, including media advertising [8], [13], [17], traffic management [2], [4], [7] and even election prediction [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Cost-Effective User Monitoring for Popularity Prediction of Online User-Generated Content

Yang

Chen

Miao

et al. 2014

2014 IEEE International Conference on Data Mining Workshop

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In this paper, we study on the popularity prediction of online user-generated contents, where high quality predictions give us much more flexibility and preparing time in deploying limited resources (such as advertising budget, monitoring capacity) into more popular contents. However the high retrieval cost of data used in prediction is a big challenge due to the large amount of users and contents involved. We propose a notion that higher popularity user-generated contents can be predicted by concentrating on fewer but informative users, as we notice the fact that contents generated by those users tend to become popular while that which are generated by the rest users do not. We develop a cost-effective popularity prediction framework to fulfil online prediction. It contains 3 modules: (a) online data retrieving, (b) informative users selection and (c) popularity prediction. A hybrid user selection algorithm and several popularity prediction algorithms/improvements are presented, and their performance are evaluated and compared using (a) the selected users' generated data and (b) all users' generated data, retrieved from Sina Weibo Microblogger. The best prediction algorithm reaches a 78% accuracy at the time of 24 hours after publishing time when level width N l equals 500. And the best combination of prediction and selection algorithms performs only about 7% worse on dataset of 2000 users than on dataset of all users (about 4.46 million).

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On the merits of popularity prediction in multimedia content caching

Cited by 20 publications

References 19 publications

Towards a predictive cache replacement strategy for multimedia content

Towards a predictive cache replacement strategy for multimedia content

Predicting the Popularity of Online Serials with Autoregressive Models

Cost-Effective User Monitoring for Popularity Prediction of Online User-Generated Content

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