Abstract
The rapid development of information system proposes higher demand for monitoring. Usually we resort to a data acquisition system to collect variety of metrics from each device for real-time anomaly detection, alerting and analysis. It is a great challenge to realize real-time and reliable data collection and gathering in a data acquisition system for large-scale system. In this paper, we propose an Adaptive window Acquisition Algorithm (AAA) to support data acquisition on great amount of data sources. AAA can dynamically adjust its policy according to the number of data sources and the acquisition interval to achieve better performance. The algorithm has been applied to a large management system project. Experimental results show that with the help of dynamic adjusting mechanism, the proposed approach can provide reliable collection service for common data acquisition systems.
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© 2016 Springer International Publishing Switzerland
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Zhou, R., Lv, Y., Fan, D., Zhang, H., Zhu, C. (2016). AAA: A Massive Data Acquisition Approach in Large-Scale System Monitoring. In: Zhan, J., Han, R., Zicari, R. (eds) Big Data Benchmarks, Performance Optimization, and Emerging Hardware. BPOE 2015. Lecture Notes in Computer Science(), vol 9495. Springer, Cham. https://doi.org/10.1007/978-3-319-29006-5_9
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DOI: https://doi.org/10.1007/978-3-319-29006-5_9
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