On bandwidth and storage tradeoffs in multimedia distribution networks

Schaffa, F.; Nussbaumer, J.-P.

doi:10.1109/infcom.1995.515978

Cited by 23 publications

(11 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We differ by studying the ongoing running cost of a video system. Schaffa and Nussbaumer study a distributed video system in which servers are configured as a hierarchical balanced tree, and all video files are replicated at a certain level in the tree [8]. While the paper studies the symmetric case in which request rates across the servers at a given tree level are the same, we make no such assumption here.…”

Section: Previous Workmentioning

confidence: 99%

“…Hence, the average storage used is (7) Regarding , if there is no request arriving within the multicast interval, the multicast stream would be aborted after minutes; otherwise it would be used for a time minutes. Therefore (8) Note that , which is one stream more than the true-VOD case. This is expected because when , streams are "started and aborted" continuously and such wastage amounts to one full stream.…”

Section: B Multicast Deliverymentioning

confidence: 99%

See 1 more Smart Citation

Distributed servers architecture for networked video services

Chan

Tobagi

2001

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

Abstract-In an on-demand video system, the video repository generally has limited streaming capacities and may be far from the users. In order to achieve higher user capacity and lower network transmission cost, distributed servers architecture can be used, in which multiple local servers are placed close to user pools and, according to their local demands, dynamically cache the contents streamed from the repository. We study in this paper a number of caching schemes as applied in the local servers depending on whether the repository is able to multicast movie contents to the local servers or not, and whether the local servers can exchange their cached contents among themselves or not. Our caching schemes keep a circular buffer of data for the movie requested, and hence movies are partially cached. By adjusting the size of the buffer, such caching is able to achieve better tradeoff between network channels and local storage as compared to the traditional caching in which a movie is treated as an entity. For each caching scheme, we study the tradeoff between the local storage and the network channels, and address how the total cost of the system can be minimized by appropriately sizing the buffer. As compared to a number of traditional operations (request batching and multicasting, true-VOD, etc.), we show that distributed servers architecture is able to achieve much lower system cost to offer on-demand video services.Index Terms-Caching schemes, distributed servers architecture, network channels and local storage tradeoff, unicast and multicast, video-on-demand.

show abstract

Section: Previous Workmentioning

confidence: 99%

Section: B Multicast Deliverymentioning

confidence: 99%

Distributed servers architecture for networked video services

Chan

Tobagi

2001

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

show abstract

“…The simpler approaches consider only the aggregate user demand; the specific objects requested are not important [3,4,15]. Another avenue is to treat objects individually, associating with each a different popularity (user demand) [5,12]. Although most of these functions account for transport costs and some for storage costs, none of them explicitly includes the number and cost of the equipment installed at each location.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…An alternative is to store only specific objects from the origin at the surrogate servers [12]. The empirical study performed in [7] indicates that approximately 80% of requests for Web objects are for 10% of the objects.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…This suggests that a relatively small amount of storage can achieve a high hit ratio if the cached content is carefully chosen. Previous studies in the distribution of multimedia files in CDNs or in VoD applications have used Zipf's Law to characterize the popularity of the different files [2,12,13]. However, empirical data indicates that a Zipf model is not a good fit for the most popular files [1,9].…”

Section: Related Work and Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Video-on-Demand Equipment Allocation

Thouin

Coates

Goodwill

Fifth IEEE International Symposium on Network Computing and Applications (NCA'06)

View full text Add to dashboard Cite

show abstract

Growing Half-Balls: Minimizing Storage and Communication Costs in CDNs

Bar-Yehuda

Kantor

Kutten

et al. 2012

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Dynamic Content Distribution problem addresses the trade-off between storage and delivery costs in modern virtual Content Delivery Networks (CDNs). That is, a video file can be stored in multiple places so that the request of each user is served from a location that is near by to the user. This minimizes the delivery costs, but is associated with a storage cost. This problem is NP-hard even in grid networks. In this paper, we present a constant factor approximation algorithm for grid networks. We also present an O(log δ)-competitive algorithm, where δ is the normalized diameter of the network, for general networks with general metrics. We show a matching lower bound by using a reduction from online undirected Steiner tree. Our algorithms use a rather intuitive approach that has an elegant representation in geometric terms.

show abstract

On bandwidth and storage tradeoffs in multimedia distribution networks

Cited by 23 publications

References 4 publications

Distributed servers architecture for networked video services

Distributed servers architecture for networked video services

Video-on-Demand Equipment Allocation

Growing Half-Balls: Minimizing Storage and Communication Costs in CDNs

Contact Info

Product

Resources

About