Joint Edge Content Cache Placement and Recommendation: Bayesian Approach

Krishnendu, S.; Bharath, B. N.; Bhatia, Vimal

doi:10.1109/vtc2021-spring51267.2021.9448631

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…To compare and contrast the obtained regret bound, we also derive an approximate regret bound on a more powerful genie aided scenario using the Point estimation method. 1 These regret bounds are shown to be data dependent. Hence, in order to get better insights, we carry out experiments to determine the scaling of the data dependent term of the regret, and show that the Bayesian estimation method is superior compared to Point estimation method.…”

Section: Introductionmentioning

confidence: 94%

Online Learning to Cache and Recommend in the Next Generation Cellular Networks

Tharakan,

Bharath,

Bhatia

2024

Trans. Mach. Learn. Comm. Netw.

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An efficient caching can be achieved by predicting the popularity of the files accurately. It is well known that the popularity of a file can be nudged by using recommendation, and hence it can be estimated accurately leading to an efficient caching strategy. Motivated by this, in this paper, we consider the problem of joint caching and recommendation in a 5G and beyond heterogeneous network. We model the influence of recommendation on demands by a Probability Transition Matrix (PTM). The proposed framework consists of estimating the PTM and use them to jointly recommend and cache the files. In particular, this paper considers two estimation methods namely a) Bayesian estimation and b) a genie aided Point estimation. An approximate high probability bound on the regret of both the estimation methods are provided. Using this result, we show that the approximate regret achieved by the genie aided Point estimation approach is O(T 2/3 √ log T ) while the Bayesian estimation method achieves a much better scaling of O( √ T ). These results are extended to a heterogeneous network consisting of M small base stations (SBSs) with a central macro base station. The estimates are available at multiple SBSs, and are combined using appropriate weights. Insights on the choice of these weights are provided by using the derived approximate regret bound in the multiple SBS case. Finally, simulation results confirm the superiority of the proposed algorithms in terms of average cache hit rate, delay and throughput.INDEX TERMS Cache placement, content delivery, recommendation, Bayesian estimation.

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Section: Introductionmentioning

confidence: 94%

Online Learning to Cache and Recommend in the Next Generation Cellular Networks

Tharakan,

Bharath,

Bhatia

2024

Trans. Mach. Learn. Comm. Netw.

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“…To strike a balance between increasing mobile traffic and user's quality-of-experience, edge caching and computing can be seen as invariable solution by bringing storage and computation close to the edge. Both edge caching and computation helps in storing and computation helps in the implementation of ML algorithms at the edge, hence making the edge intelligent and further reducing the burden on the backhaul (see [6], [7], [8], or [9]). The standard simplified algorithms such as least frequently used (LFU), least recently used (LRU), least frequently/recently used (LRFU) and other variants, can be inefficient when it comes to dynamic environments, since they do not take into account the correlation and non-stationarity of the demand requests.…”

Section: Introductionmentioning

confidence: 99%