Abstract
High concurrent analytic applications such as SaaS based BI services face the problem of how to meet performance SLAs (Service Level Agreement) when the number of users and concurrency increased. In order to reduce the task processing overheads and services response time, analytic applications tend heavily rely on various main-memory data management and cache techniques. In this paper, we designed a cost-conscious cache replacement approach named CRSR (Cost-conscious Result Sets Replacement), which take task result sets as the essential data unit and replace the existing result set by a specialized cost estimation strategy. We conduct a series of evaluation to compare the proposed CRSR approach with representative cache management methods, the experiments show that in most cases, the proposed CRSR algorithm can efficiently reduce the response time of high concurrency analysis service and outperform its competitors.
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Acknowledgments
This work was supported by the China Ministry of Science and Technology under the State Key Development Program for Basic Research (2012CB821800), Fund of National Natural Science Foundation of China (No. 61462012, 61562010), the Joint Research Fund in Astronomy under cooperative agreement between the National Natural Science Foundation of China and Chinese Academy of Sciences (No. U1531246), the Strategic Priority Research Program “The Emergence of Cosmological Structures” of the Chinese Academy of Sciences (No. XDB09000000), High Tech. Project Fund of Guizhou Development and Reform Commission (No. [2013]2069), Industrial Research Projects of the Science and Technology Plan of Guizhou Province (No. GY[2014]3018), the Major Applied Basic Research Program of Guizhou Province (No. JZ20142001, JZ20142001-01, JZ20142001-05).
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Min, S., Li, H., Chen, M., Dai, Z., Zhu, M. (2016). Accelerating Concurrent Analytic Tasks with Cost-Conscious Result Set Replacement Algorithm. In: Wang, G., Ray, I., Alcaraz Calero, J., Thampi, S. (eds) Security, Privacy and Anonymity in Computation, Communication and Storage. SpaCCS 2016. Lecture Notes in Computer Science(), vol 10067. Springer, Cham. https://doi.org/10.1007/978-3-319-49145-5_28
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DOI: https://doi.org/10.1007/978-3-319-49145-5_28
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