PPC: Popularity Prediction Caching in ICN

Zhang, Yuanzun; Tan, Xiaobin; Li, Weiping

doi:10.1109/lcomm.2017.2731312

Cited by 39 publications

(25 citation statements)

References 10 publications

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“…The parameters used in the simulations are listed in Table IV. These parameters were selected based on the previous studies [18] [20] [25] [26]. In total, ten experiments were performed, and a 95% confidence interval was calculated.…”

Section: Simulation Resultsmentioning

confidence: 99%

Name Popularity Algorithm: A Cache Replacement Strategy for NDN Networks

Silva

Araujo²,

Linder³

et al. 2019

JCIS

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An essential aspect of the network paradigm called Named Data Networking (NDN) is that all nodes in the network have the ability to cache contents natively. The focus of this work relies on efficiently choosing which content should be removed from a full cache to allow storing another one. We propose a new caching replacement algorithm for NDN networks called Name Popularity Algorithm (NPA). The proposed algorithm adopts an extra table that takes into account a long time history for the popularity of contents. Thus, when a data content is removed from the cache, its popularity is not reset, improving the cache usage of the network routers. The performance of NPA is compared to well-known strategies in the literature through NS-3/ndnSIM simulations. The results indicate that the proposed algorithm overcomes others with lower end-to-end delay, higher cache hit ratio and smaller hop count. Besides, our code is available as open source to be accessible for other researchers and to allow reproduction of the results.

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Section: Simulation Resultsmentioning

confidence: 99%

Name Popularity Algorithm: A Cache Replacement Strategy for NDN Networks

Silva

Araujo²,

Linder³

et al. 2019

JCIS

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“…The behavior of the user directly affects the propagation characteristics of the content, so the popularity of the content can also be characterized according to the behavior of the user. The PPC policy uses video user request behavior and leaving behavior as the basis for popularity to measure the relationship between adjacent blocks in the same video file [23]. The popularity is defined as the request behavior and the departure behavior time of a large number of users for a certain content block, and the size of the target content block is used as the mediation of the content block.…”

Section: Popularity-based Cachingmentioning

confidence: 99%

Analysis of research on satellite network caching mechanism based on ICN

Liu

2020

MATEC Web Conf.

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ICN was introduced into satellite networks to meet the growing demand for content acquisition. The caching mechanism is one of the most critical parts that profoundly affects the performance of the network. Based on the application background of satellite networks, the working mechanism of ICN and its technical advantages in introducing satellite networks are elaborated. The application status of ICN cache in satellite networks is analyzed to explore the applicability of existing caching mechanism in satellite networks, and the development and challenges of ICN caching mechanism in satellite network are analyzed.

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“…In this section, three different models are presented for comparison. These models with some minor variations have been employed in the literature [12]- [15].…”

Section: Models For Comparisonmentioning

confidence: 99%

“…[14] models popularity using auto regressive (AR) model to predict the number of requests in the time series. [15] predicts content requests for video segments using a linear model. In [16], a context-aware online policy has been presented which learns content popularity independently across contents.…”

Section: Introductionmentioning

confidence: 99%

Online Learning Models for Content Popularity Prediction in Wireless Edge Caching

Garg

Sellathurai

Bettagere

et al. 2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

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Caching popular contents in advance is an important technique to achieve the low latency requirement and to reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process (PPP), optimal content placement caching probabilities are derived for known popularity profile, which is unknown in practice. In this paper, online prediction (OP) and online learning (OL) methods are presented based on popularity prediction model (PPM) and Grassmannian prediction model (GPM), to predict the content profile for future time slots for time-varying popularities. In OP, the problem of finding the coefficients is modeled as a constrained nonnegative least squares (NNLS) problem which is solved with a modified NNLS algorithm. In addition, these two models are compared with log-request prediction model (RPM), information prediction model (IPM) and average success probability (ASP) based model. Next, in OL methods for the time-varying case, the cumulative mean squared error (MSE) is minimized and the MSE regret is analyzed for each of the models. Moreover, for quasi-time varying case where the popularity changes block-wise, KWIK (know what it knows) learning method is modified for these models to improve the prediction MSE and ASP performance. Simulation results show that for OP, PPM and GPM provides the best ASP among these models, concluding that minimum mean squared error based models do not necessarily result in optimal ASP. OL based models yield approximately similar ASP and MSE, while for quasi-time varying case, KWIK methods provide better performance, which has been verified with MovieLens dataset.

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PPC: Popularity Prediction Caching in ICN

Cited by 39 publications

References 10 publications

Name Popularity Algorithm: A Cache Replacement Strategy for NDN Networks

Name Popularity Algorithm: A Cache Replacement Strategy for NDN Networks

Analysis of research on satellite network caching mechanism based on ICN

Online Learning Models for Content Popularity Prediction in Wireless Edge Caching

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