Abstract
Cloud service discovery forms the foundation of the efficient and agile implementation of complex business processes. The core problem of existing QoS-aware cloud service discovery mechanisms is that the process of cloud service QoS acquisition is difficult. The issue of how to obtain the number of times a cloud service has been accessed over a period of time needs to be addressed, and the access information for the cloud service needs to be fully recorded. It is difficult to adapt traditional means of data processing to the concurrent access requirements of a massive cloud service, resulting in a lack of accurate QoS information support for cloud service aggregation. This paper proposes a method based on bucket filtering to collect cloud service access flow log information. It then explores a way of abstracting cloud service access flow into a binary bit stream, and uses the DGIM algorithm to carry out an approximate evaluation of cloud service access to analyse cloud service access flow. Our approach enables an estimation of cloud service access frequency and balances the space and time overheads of cloud service access log storage and calculation. Theoretical analysis and experimental verification prove that our access has good universality and good performance.
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Acknowledgment
This work was supported in part by the Natural Science Foundation of China with No.61802344, in part by Zhejiang Provincial Natural Science Foundation of China with No. LY16F030012 and LY15F030016, in part by Humanities and Social Science Foundation of Ministry of Education of China with No. 16YJCZH112 and in part by Ningbo Science and Technology Special Projects of China with No. 2016C11024 and 2017C110002.
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Wen, S., Yang, J., Zhu, C., Chen, G. (2020). Cloud Service Access Frequency Estimation Based on a Stream Filtering Method. In: U, L., Yang, J., Cai, Y., Karlapalem, K., Liu, A., Huang, X. (eds) Web Information Systems Engineering. WISE 2020. Communications in Computer and Information Science, vol 1155. Springer, Singapore. https://doi.org/10.1007/978-981-15-3281-8_12
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DOI: https://doi.org/10.1007/978-981-15-3281-8_12
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