TY - JOUR
T1 - Content Popularity Prediction Towards Location-Aware Mobile Edge Caching
AU - Yang, Peng
AU - Zhang, Ning
AU - Zhang, Shan
AU - Yu, Li
AU - Zhang, Junshan
AU - Shen, Xuemin Sherman
N1 - Funding Information:
Manuscript received September 15, 2017; revised February 12, 2018 and May 29, 2018; accepted August 30, 2018. Date of publication September 17, 2018; date of current version March 22, 2019. This work was supported in part by the National Natural Science Foundation of China under Grants 61871437 and 61801011 and in part by the Natural Sciences and Engineering Research Council of Canada. The work of P. Yang was supported by the China Scholarship Council. This paper was presented in part at the IEEE Global Communications Conference, Singapore, Dec. 4–8, 2017. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Xiaoqing Zhu. (Corresponding author: Li Yu.) P. Yang and L. Yu are with the School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail:,yangpeng@hust.edu.cn; hustlyu@hust.edu.cn).
Publisher Copyright:
© 1999-2012 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an H∞ filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.
AB - Mobile edge caching aims to enable content delivery within the radio access network, which effectively alleviates the backhaul burden and reduces response time. To fully exploit edge storage resources, the most popular contents should be identified and cached. Observing that user demands on certain contents vary greatly at different locations, this paper devises location-customized caching schemes to maximize the total content hit rate. Specifically, a linear model is used to estimate the future content hit rate. For the case with zero-mean noise, a ridge regression-based online algorithm with positive perturbation is proposed. Regret analysis indicates that the hit rate achieved by the proposed algorithm asymptotically approaches that of the optimal caching strategy in the long run. When the noise structure is unknown, an H∞ filter-based online algorithm is devised by taking a prescribed threshold as input, which guarantees prediction accuracy even under the worst-case noise process. Both online algorithms require no training phases and, hence, are robust to the time-varying user demands. The estimation errors of both algorithms are numerically analyzed. Moreover, extensive experiments using real-world datasets are conducted to validate the applicability of the proposed algorithms. It is demonstrated that those algorithms can be applied to scenarios with different noise features, and are able to make adaptive caching decisions, achieving a content hit rate that is comparable to that via the hindsight optimal strategy.
KW - Mobile edge computing
KW - dynamic content caching
KW - location awareness
KW - popularity prediction
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U2 - 10.1109/TMM.2018.2870521
DO - 10.1109/TMM.2018.2870521
M3 - Article
AN - SCOPUS:85053313934
VL - 21
SP - 915
EP - 929
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
SN - 1520-9210
IS - 4
M1 - 8466606
ER -