Distributed Caching Algorithms for Content Distribution Networks

Borst, Sem; Gupta, Varun; Walid, Anwar

doi:10.1109/infcom.2010.5461964

Cited by 576 publications

(492 citation statements)

References 14 publications

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“…The previous studies have considered content popularity for service deployment [14,15]. In those approaches, content caching is based on popularity of content.…”

Section: Related Workmentioning

confidence: 99%

Community Model for Smart TV over the Top Services

2016

J Inf Process Syst

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We studied the current state-of-the-art of Smart TV, the challenges and the drawbacks. Mainly we discussed the lack of end-to-end solution. We then illustrated the differences between Smart TV and IPTV from network service provider point of view. Unlike IPTV, viewer of Smart TV's over-the-top (OTT) services could be global, such as foreign nationals in a country or viewers having special viewing preferences. Those viewers are sparsely distributed. The existing TV service deployment models over Internet are not suitable for such viewers as they are based on content popularity, hence we propose a community based service deployment methodology with proactive content caching on rendezvous points (RPs). In our proposal, RPs are intermediate nodes responsible for caching routing and decision making. The viewer's community formation is based on geographical locations and similarity of their interests. The idea of using context information to do proactive caching is itself not new, but we combined this with "in network caching" mechanism of content centric network (CCN) architecture. We gauge the performance improvement achieved by a community model. The result shows that when the total numbers of requests are same; our model can have significantly better performance, especially for sparsely distributed communities.

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“…The previous studies have considered content popularity for service deployment [14,15]. In those approaches, content caching is based on popularity of content.…”

Section: Related Workmentioning

confidence: 99%

Community Model for Smart TV over the Top Services

2016

J Inf Process Syst

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“…More recent content placement schemes using an analytical framework include the work by Valancius et al [16], which proposes an LP-based heuristic to identify which videos and how many replicas to be placed at customer home gateways. Borst et al [8] focus on minimizing link bandwidth utilization assuming a tree structure with limited depth. Both of these proposals focus on the delivery from the server to viewers; instead, we consider placing content among the servers in different locations.…”

Section: Related Workmentioning

confidence: 99%

“…For example, many approaches [7,8,16] use optimization-based techniques to guide placement. These, however, resort to heuristics to solve the problem or ignore important constraints such as link bandwidth.…”

Section: Introductionmentioning

confidence: 99%

Leveraging Video Viewing Patterns for Optimal Content Placement

et al. 2012

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Abstract. As IP becomes the predominant choice for video delivery, storing the ever increasing number of videos for delivery will become a challenge. In this paper we focus on how to take advantage of user viewing patterns to place content in provider networks to reduce their storage and network utilization. We first characterize user viewing behavior using data collected from a nationally deployed Video-on-Demand service. We provide proof that users watch only a small portion of videos (not just for short clips, but even with full-length movies). We use this information and a highly flexible Mixed Integer Programming (MIP) formulation to solve the placement problem, in contrast to traditional popularity-based placement and caching strategy. We perform detailed simulations using real traces of user viewing sessions (including stream control operations such as Pause, Skip, etc.). Our results show that the use of a segmentbased placement yields substantial savings both in storage as well as network bandwidth. For example, compared to a simple caching scheme using full videos, our MIP-based placement using segments can achieve up to 71% reduction in peak link bandwidth usage.

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“…Despite potential high rate in the new architectures, traffic congestion might occur during peak-traffic times. A promising solution to reduce latency and network costs of content delivery is to bring content closer to end users via distributed storage across the network, which is referred to content placement or caching [2] and usually consists of two phases: placement and delivery. In the former, popular content is duplicated and stored in distributed caches in the network.…”

Section: Introductionmentioning

confidence: 99%

“…In this manner, caching allows This work is supported in part by Luxembourg FNR Core program under the project code I2R-SIG-PFN-13SEMI and in part by European Union funding under the project code I2R-SIG-PEU-15SNSA. significant throughput reduction during peak-traffic times and thus reducing network congestion [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient design for edge-caching wireless networks: When is coded-caching beneficial?

Chatzinotas

Ottersten

2017

2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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Abstract-Content caching is an efficient technique to reduce delivery latency and system congestion during peak-traffic times by bringing data closer to end users. Existing works consider caching only at higher layers separated from physical layer. In this paper, we study wireless caching networks by taking into account cache capability when designing the signal transmission. In particular, we investigate multi-layer caching and their performance in edge-caching wireless networks where both base station (BS) and users are capable of storing content data in their local cache. Two notable uncoded and coded caching strategies are studied. Firstly, we propose a coded caching strategy that is applied to arbitrary value of cache size. The required backhaul and access rates are given as a function of the BS and user cache size. Secondly, closed-form expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Thirdly, the system EE is maximized via precoding vectors design and optimization while satisfying the user request rate. Finally, numerical results are presented to verify the effectiveness of the two caching methods.

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Distributed Caching Algorithms for Content Distribution Networks

Cited by 576 publications

References 14 publications

Community Model for Smart TV over the Top Services

Community Model for Smart TV over the Top Services

Leveraging Video Viewing Patterns for Optimal Content Placement

Energy-efficient design for edge-caching wireless networks: When is coded-caching beneficial?

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