Performance of Caching Algorithms for IPTV On-Demand Services

Vleeschauwer, Danny De; Laevens, Koenraad

doi:10.1109/tbc.2009.2015983

Cited by 57 publications

(28 citation statements)

References 15 publications

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“…Based on comment data and the co-participation network, they are able to accurately predict the future popularity of any news item shortly after it has been posted. In the context of VoD services, De Vleeschauwer & Laevens [2] propose a prediction method based on a generic user-demand model derived from traces of VoD and catch-up TV services. Wu et al [27] adapted the previously mentioned reservoir computing approach to the popularity prediction of multimedia content.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional strategies, such as Least Recently Used (LRU) and Least Frequently Used (LFU), assume that what was most popular in the past, will also be most popular in the future. However, the popularity of multimedia content is known to be highly dynamic [2]. Consequently, caching efficiency can be further increased by taking these dynamics into account and actually try to predict future popularity instead of directly applying historical information.…”

Section: Introductionmentioning

confidence: 99%

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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 Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Towards a predictive cache replacement strategy for multimedia content

Famaey

Iterbeke

Wauters

et al. 2013

Journal of Network and Computer Applications

View full text Add to dashboard Cite

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“…Dimensioning of caches for on-demand television services has been studied in [19] in the context of a cable television distribution network, in [9] in the context of Internet TV and in [8], [13], [18] in an IPTV context. Often, when determining the cache size, it is assumed that the popularity distribution of the offered multimedia content is a priori known and static.…”

Section: Introductionmentioning

confidence: 99%

“…Yet, in reality, the popularity of the objects is not known by the cache and evolves over time. Therefore the popularity of objects needs to be predicted, measured and tracked over time by monitoring and aggregating the user requests for the objects [8]. The new interactive television services pose new requirements on the cache and the caching algorithm, because the typical lifetime of the objects is different from the one of web objects and the objects themselves are much larger.…”

Section: Introductionmentioning

confidence: 99%

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Performance analysis of a caching algorithm for a catch-up television service

Avramova

Vleeschauwer²,

Wittevrongel

et al. 2010

Multimedia Systems

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The Catch-Up TV (CUTV) service allows users to watch video content that was previously broadcast live on TV channels and later placed on an on-line video store. Upon a request from a user to watch a recently missed episode of his/her favourite TV series, the content is streamed from the video server to the customer's receiver device. This requires that an individual flow is set up for the duration of the video, and since it is hard to impossible to employ multicast streaming for this purpose (as users seldomly issue a request for the same episode at the same time), these flows are unicast.In this paper we demonstrate that with the growing popularity of the CUTV service, the number of simultaneously running unicast flows on the aggregation parts of the network threaten to lead to an unwieldy increase in required bandwidth. Anticipating this problem and trying to alleviate it, the network operators deploy caches in strategic places in the network. We investigate the performance of such a caching strategy and the impact of its size and the cache update logic.We first analyse and model the evolution of video popularity over time based on traces we collected during ten months. Through simulations we compare the performance of the traditional LRU (Least-Recently Used) and LFU (Least-Frequently Used) caching algorithms to our own algorithm. We also compare their performance with a "perfect" caching algorithm, which knows and hence does not have to estimate the video request rates.In the experimental data we see that the video parameters from the popularity evolution law can be clustered.Therefore we investigate theoretical models than can capture these clusters and we study the impact of clustering on the caching performance. Finally, some considerations on the optimal cache placement are presented.

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Optimum caching strategies for a telco CDN

Vleeschauwer¹,

Robinson²

2011

Bell Labs Tech. J.

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In order to cope with the rapidly increasing video traffic (both downloaded and streamed) during which the user reads or thinks about the content just downloaded-in the former, the content is consumed (i.e., watched, heard) as it is downloading. It follows that video cannot rely on statistical multiplexing as much as data can, and this, consequently, puts even more stress on the network. Both types of services (i.e., data and video) can benefit from a content distribution network (CDN). The principle of a CDN is that objects that require a large (and sustained) bit rate to be delivered are cached in a CDN point of presence (PoP) close to the end user. For data services this decreases the download time and hence improves the user's quality of experience. Moreover, CDNs also offload traffic requests from the origin servers and, hence, provide

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Performance of Caching Algorithms for IPTV On-Demand Services

Cited by 57 publications

References 15 publications

Towards a predictive cache replacement strategy for multimedia content

Towards a predictive cache replacement strategy for multimedia content

Performance analysis of a caching algorithm for a catch-up television service

Optimum caching strategies for a telco CDN

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