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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Vydané v:IEEE internet of things journal Ročník 11; číslo 23; s. 38425 - 38440
Hlavní autori: Wang, Yitu, Chen, Qi, Wang, Wei, Nakachi, Takayuki, Zhang, Guangchen, Liou, Juinjei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.12.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2024.3446591