Content-Caching-Oriented Popularity Forecast and User Clustering

Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast a...

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Vydáno v:	IEEE internet of things journal Ročník 11; číslo 23; s. 38425 - 38440
Hlavní autoři:	Wang, Yitu, Chen, Qi, Wang, Wei, Nakachi, Takayuki, Zhang, Guangchen, Liou, Juinjei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.12.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Algorithms Caching Clustering Clustering algorithms Complexity theory content caching Gaussian process Gaussian process (GP) Iterative algorithms K-means popularity forecast Prediction algorithms Predictive models Servers Time series analysis
ISSN:	2327-4662, 2327-4662
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Abstract	Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast and user clustering algorithms for improving the caching performance. Specifically, through analyzing the caching performance drop incurred by inaccurate popularity forecast from the Bayesian perspective, we obtain two critical insights, which trigger the following designs: 1) as the utility of forecast varies according to the content rank, we propose a content-caching-oriented popularity forecast algorithm based on Gaussian process (GP), where more computational resource is allocated to forecast the popularity of prioritized contents and 2) to alleviate the influence of forecast error on the rank of prioritized contents, we propose a content-caching-oriented user clustering algorithm based on the K-means algorithm. Since the involved optimization problem is NP-hard, we propose an iterative algorithm, whose convergence property in terms of region stability is proved, as the objective function may vary before a local minima is reached. Finally, the simulation results demonstrate the superiority of the proposed framework.
AbstractList	Content popularity forecast is a key enabler toward the realization of proactive content caching, contributing to significant reduction of content fetching delay. Different from most of the existing literature that concentrating on enhancing the forecast accuracy, we tailor the popularity forecast and user clustering algorithms for improving the caching performance. Specifically, through analyzing the caching performance drop incurred by inaccurate popularity forecast from the Bayesian perspective, we obtain two critical insights, which trigger the following designs: 1) as the utility of forecast varies according to the content rank, we propose a content-caching-oriented popularity forecast algorithm based on Gaussian process (GP), where more computational resource is allocated to forecast the popularity of prioritized contents and 2) to alleviate the influence of forecast error on the rank of prioritized contents, we propose a content-caching-oriented user clustering algorithm based on the K-means algorithm. Since the involved optimization problem is NP-hard, we propose an iterative algorithm, whose convergence property in terms of region stability is proved, as the objective function may vary before a local minima is reached. Finally, the simulation results demonstrate the superiority of the proposed framework.
Author	Wang, Yitu Nakachi, Takayuki Chen, Qi Liou, Juinjei Wang, Wei Zhang, Guangchen
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SubjectTerms	Accuracy Algorithms Caching Clustering Clustering algorithms Complexity theory content caching Gaussian process Gaussian process (GP) Iterative algorithms K-means popularity forecast Prediction algorithms Predictive models Servers Time series analysis
Title	Content-Caching-Oriented Popularity Forecast and User Clustering
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