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PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center

Kuo-Chi Fang, Husnu S. Narman, Ibrahim Hussein Mwinyi, Wook-Sung Yoo
Copyright: © 2019 |Volume: 11 |Issue: 1 |Pages: 13
ISSN: 1941-8663|EISSN: 1941-8671|EISBN13: 9781522565482|DOI: 10.4018/IJITN.2019010102
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MLA

Fang, Kuo-Chi, et al. "PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center." IJITN vol.11, no.1 2019: pp.17-29. http://doi.org/10.4018/IJITN.2019010102

APA

Fang, K., Narman, H. S., Mwinyi, I. H., & Yoo, W. (2019). PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center. International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 11(1), 17-29. http://doi.org/10.4018/IJITN.2019010102

Chicago

Fang, Kuo-Chi, et al. "PPHA-Popularity Prediction Based High Data Availability for Multimedia Data Center," International Journal of Interdisciplinary Telecommunications and Networking (IJITN) 11, no.1: 17-29. http://doi.org/10.4018/IJITN.2019010102

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Abstract

Due to the growth of internet-connected devices and extensive data analysis applications in recent years, cloud computing systems are largely utilized. Because of high utilization of cloud storage systems, the demand for data center management has been increased. There are several crucial requirements of data center management, such as increase data availability, enhance durability, and decrease latency. In previous works, a replication technique is mostly used to answer those needs according to consistency requirements. However, most of the works consider full data, popular data, and geo-distance-based replications by considering storage and replication cost. Moreover, the previous data popularity based-techniques rely on the historical and current data access frequencies for replication. In this article, the authors approach this problem from a distinct aspect while developing replication techniques for a multimedia data center management system which can dynamically adapt servers of a data center by considering popularity prediction in each data access location. Therefore, they first label data objects from one to ten to track access frequencies of data objects. Then, they use those data access frequencies from each location to predict the future access frequencies of data objects to determine the replication levels and locations to replicate the data objects, and store the related data objects to close storage servers. To show the efficiency of the proposed methods, the authors conduct an extensive simulation by using real data. The results show that the proposed method has an advantage over the previous works in terms of data availability and increases the data availability up to 50%. The proposed method and related analysis can assist multimedia service providers to enhance their service qualities.

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