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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/12868
Title: Application of time series to performance assurance of Big Data environment
Authors: Balasanov, Y.
Zibitsker, B.
Bakanas, T.
Hammond, E.
Islas-Martinez, M.
Keywords: материалы конференций;time series data;anomaly detection;performance prediction;big data;performance assurance
Issue Date: 2017
Publisher: БГУИР
Citation: Application of time series to performance assurance of Big Data environment / Y. Balasanov [and other] // BIG DATA and Advanced Analytics: collection of materials of the third international scientific and practical conference, Minsk, Belarus, May 3–4, 2017 / editorial board : М. Batura [et al.]. – Minsk : BSUIR, 2017. – С. 47-62.
Abstract: The selection of the Big Data algorithms, YARN rules and infrastructure can affect accuracy, performance and scalability of Big Data Applications. We will present a methodology and algorithms for proactive performance management. Every hour collected measurement data are aggregated into workloads representing each lines of business. Each workload has three profiles, including 1) performance (response time and throughput), 2) resource utilization and 3) data usage profiles. Profiles represent Workloads’ Time series. This information is used as input for exploratory analysis techniques specific to time series data. The data are transformed into stationary Time Series and an analysis to select the best time series model (ARMA, VARMA) is conducted. Historical data are used to identify past exceedances which are utilized as predictors or outcome variables to build a classification model. We will review short term prediction, seasonal peaks identification, diagnostic and root cause analysis Performance Assurance algorithms enabling proactive performance management of Big Data Application.
URI: https://libeldoc.bsuir.by/handle/123456789/12868
Appears in Collections:BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2017)

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