Analysis of resource sharing in information providing services

Gelman, A.D.; Halfin, S.

doi:10.1109/glocom.1990.116528

Cited by 39 publications

(17 citation statements)

References 5 publications

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“…Thus there is no competition for the channels among the various videos. As a result, the scheduling policies studied in [12] are when to playa given video rather than which video to play on any available channel. The notion of unfairness was also not considered in the allocation of channels to various videos.…”

Section: Received November 1994mentioning

confidence: 99%

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Dynamic batching policies for an on-demand video server

1996

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Section: Received November 1994mentioning

confidence: 99%

“…A different model of batching in the context of videoon-demand systems with impatient customers was studied in [12](see also references therein). In this case it was assumed that a prespecified number of channels was available for each video.…”

Section: Received November 1994mentioning

confidence: 99%

Dynamic batching policies for an on-demand video server

1996

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“…Moreover, given an arrival within the interval , the time at which the user arrives is uniformly distributed and hence the probability that the user arrives at time is (Poisson property). Therefore, the average length of the unicast streams is , and the average number of unicast streams required, , is given by (by Little's formula) (2) which yields (3) To minimize , we differentiate it with respect to and set it to zero to obtain . Note that in the traditional on-demand system based on patching, users cannot tolerate any delay and hence , where is a unit-step step function given by when and when .…”

Section: B Scheme Analysismentioning

confidence: 99%

Broadcasting video with the knowledge of user delay preference

Chan

Yeung

2003

IEEE Trans. on Broadcast.

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Abstract-In designing a video broadcasting system, the delay preference of a user is traditionally regarded as unknown. In fact, such preference can be known upon user's arrival by employing some techniques such as i) delay-dependent charging, where users are offered different levels of pay-per-view (PPV) depending on the maximum delay they are willing to tolerate; or ii) reservation, where a user specifies the exact play-time of a movie in advance, and he/she is charged according to the length of the reservation period. We explore, for the first time, the impact of such delay knowledge on request scheduling and system cost in terms of user loss and stream requirement. For delay-dependent charging, we propose "Delay-Aware Broadcasting" (DAB) and its variant based on reservation (DAB-r), where allocation of server streams is driven by the delay tolerance of a user. DAB-r offers differentiated grade of services according to user PPVs (and thereof classes). As compared with a system where user delay preference is not known, our schemes achieve substantially lower user loss rate, higher revenue, and better fairness. Regarding reservation system, we consider a scheme where clients can pre-buffer video data. Unicast streams are used to merge requests back to the on-going broadcast streams. We show that a reservation system achieves substantially lower stream requirement as compared to an on-demand system based on "patching."Index Terms-Delay-aware broadcasting, delay preference, reservation scheme, stream requirement, video broadcasting.

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“…One could also conceive a number of models for the user reneging behavior, as has been done in the literature: the time that a user is willing to wait before reneging is considered to be distributed according to some cumulative distribution function. Examples of the function are exponential function [7], [8], truncated Gaussian [9], and linear function (corresponding to a uniform distribution) [10]. (We note that the use of any specific model in the literature has been either arbitrary, or driven by the need to keep the underlying analysis tractable.)…”

Section: Introductionmentioning

confidence: 99%

“…Such capacity was considered to be given, and in a sense already paid for, and therefore of interest was the issue of assigning the available streams to the various requests so as to meet a certain loss rate of requests [7]- [11]. We focus in our study on network multicasting cost, in which case it is important to address the conflicting goals between high system profit and low user delay (or low user loss).…”

Section: Introductionmentioning

confidence: 99%

Tradeoff between system profit and user delay/loss in providing near video-on-demand service

Chan

Tobagi

2001

IEEE Trans. Circuits Syst. Video Technol.

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In a near video-on-demand (near-VOD) system, requests for a movie arriving in a period of time are grouped (or "batched") together and served with a single multicast stream. In this paper, we consider providing near-VOD services when there is a cost associated with using a network multicast channel. We address the tradeoff between system profit, given by the total pay-per-view collected minus the total channel cost, and user delay or user loss (due to reneging). We first analyze and compare the tradeoff of two traditional "basic" schemes, namely, the window-based schemes in which a maximum user delay can be guaranteed, and the batch-size based scheme in which system profit can be guaranteed. By combining these basic schemes, we present a scheme which can adaptively balance system profit and user delay when the underlying request rate fluctuates. We then consider the case in which delayed users may renege and determine how system profit can be maximized by sizing the batching period given user's reneging behavior. We show that maximizing profit can lead to excessively high user loss rate, especially when the channel cost is high and users are not very patient. Therefore, a shorter suboptimal batching period should be used for this case in reality. We finally introduce schemes which are able to offer high profit or low user loss when the underlying arrival rate fluctuates.Index Terms-Multicasting, near video-on-demand, request batching, system profit, user delay, user loss.

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Analysis of resource sharing in information providing services

Cited by 39 publications

References 5 publications

Dynamic batching policies for an on-demand video server

Dynamic batching policies for an on-demand video server

Broadcasting video with the knowledge of user delay preference

Tradeoff between system profit and user delay/loss in providing near video-on-demand service

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