Caching Partial Files for Content Delivery

Narayana, V. Surya; Jain, S. K.; Moharir, Sharayu

doi:10.1109/ncc.2019.8732266

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Several other approaches have been proposed, including online caching based on the history of request arrivals [23], federated learning-based caching [24], dynamic probabilistic caching policies [14], caching models accommodating timevarying request rates [25], online caching algorithms based on content request predictions [26], and joint optimization of service caching and routing [27]. Meanwhile, [12] and [28] accounted for temporal changes in content popularity by modelling the content request process as a stochastic process whose intensity is modulated by an underlying discrete-time Markov chain. The critical difference between our work and [12], [14], [23]- [28] is our assumption that new contents stochastically arrive and the existing ones expire after random lifetimes.…”

Section: A Related Workmentioning

confidence: 99%

“…Meanwhile, [12] and [28] accounted for temporal changes in content popularity by modelling the content request process as a stochastic process whose intensity is modulated by an underlying discrete-time Markov chain. The critical difference between our work and [12], [14], [23]- [28] is our assumption that new contents stochastically arrive and the existing ones expire after random lifetimes.…”

Section: A Related Workmentioning

confidence: 99%

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Caching Dynamic Contents via Mortal Restless Bandits

Vasantha,

Singh

2023

Preprint

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<p>We study content caching in a network consisting of a server and a base station with a finite-capacity cache. Contents are dynamic. They stochastically arrive in the system, stay for random times, and their popularities also randomly vary while they are alive. Fetching contents from the server and storing in the base station cache incurs a cost. But not having the requested contents at the base station also incurs a cost that reflects QoS deficiency. We study optimal proactive caching to minimise the time-average content miss and caching costs. We formulate this problem as an average cost Markov decision problem that is a restless multi-armed bandit problem. We argue that the problem is indexable and explicitly derives the Whittle indices. Finally, we demonstrate the efficacy of the Whittle index policy via numerical evaluation .</p>

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Caching Dynamic Contents via Mortal Restless Bandits

Vasantha,

Singh

2023

Preprint

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“…The authors in [21] study joint optimization of service caching and routing when request arrivals are Poisson process. The authors in [12] and [22] account for temporal changes in content popularity by modelling the content request process as a stochastic process whose intensity is modulated by an underlying discrete-time Markov chain. The key difference between our work and [12], [14], [17]- [22] is our assumption that new contents stochastically arrive and the existing ones expire after random lifetimes.…”

Section: Introductionmentioning

confidence: 99%

Caching Dynamic Contents via Mortal Restless Bandits

Krishna,

Singh

2023

2023 21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

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We study content caching in a network consisting of a server and a base station with a finite capacity cache. Contents are dynamic. They stochastically arrive in the system, stay for random times, and their popularities also randomly vary while they are alive. Fetching contents from the server and storing in the base station cache incurs a cost. But not having the requested contents at the base station also incurs a cost that reflects QoS deficiency. We study optimal proactive caching aiming at minimizing the time-average content miss and caching costs. We formulate this problem as an average cost Markov decision problem that is a restless multi-armed bandit problem. We argue that the problem is indexable and explicitly derive the Whittle indices. Finally, we demonstrate efficacy of the Whittle index policy via numerical evaluation.

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“…The authors of [9] and [10] proposed dynamic delivery of video chunks having different qualities assuming that popular contents are entirely cached. Caching of partial files is presented in [11] when popularity of video segments is different; however, different quality levels for segments are not considered. The goal of this paper is to develop the policy of caching content fractions (e.g., video chunks) and to analyze the impact of caching content fractions on the expected video quality.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Device Caching of Content Fractions on Expected Content Quality

Kim¹,

Choi²

2021

Preprint

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This paper explores caching of fractions of a video content, not caching of an entire content, to increase the expected video quality. We first show that the highest-quality content is better to be cached and propose the caching policy of video chunks having different qualities. Our caching policy utilizes the characteristics of video contents that video files can be encoded into multiple versions with different qualities, each file consists of many chunks, and chunks can have different qualities. Extensive performance evaluations are conducted to show that caching of content fractions, rather than an entire content, can improve the expected video quality especially when the channel conditions is sufficiently good to cooperate with nearby BS or helpers.

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Caching Partial Files for Content Delivery

Cited by 5 publications

References 18 publications

Caching Dynamic Contents via Mortal Restless Bandits

Caching Dynamic Contents via Mortal Restless Bandits

Caching Dynamic Contents via Mortal Restless Bandits

Impacts of Device Caching of Content Fractions on Expected Content Quality

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