Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/138651
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | OL-MEDC: An Online Approach for Cost-effective Data Caching in Mobile Edge Computing Systems |
Author: | Xia, X. Chen, F. He, Q. Cui, G. Grundy, J. Abdelrazek, M. Bouguettaya, A. Jin, H. |
Citation: | IEEE Transactions on Mobile Computing, 2023; 22(3):1646-1658 |
Publisher: | Institute of Electrical and Electronics Engineers |
Issue Date: | 2023 |
ISSN: | 1536-1233 1558-0660 |
Statement of Responsibility: | Xiaoyu Xia, Feifei Chen, Qiang He, Guangming Cui, John Grundy, S Mohamed Abdelrazek, Athman Bouguettaya, and Hai Jin |
Abstract: | Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41% on average. |
Keywords: | Data caching; mobile edge computing; online algorithm; cost-effective |
Rights: | © 2021 IEEE. |
DOI: | 10.1109/TMC.2021.3107918 |
Grant ID: | http://purl.org/au-research/grants/arc/DP200102491 http://purl.org/au-research/grants/arc/FL190100035 |
Published version: | http://dx.doi.org/10.1109/tmc.2021.3107918 |
Appears in Collections: | Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.