AUGURY: A Time Series Based Application for the Analysis and Forecasting of System and Network Performance Metrics

Abstract

This paper presents AUGURY, an application for the analysis of monitoring data from computers, servers or cloud infrastructures. The analysis is based on the extraction of patterns and trends from historical data, using elements of time-series analysis. The purpose of AUGURY is to aid a server administrator by forecasting the behaviour and resource usage of specific applications and in presenting a status report in a concise manner. AUGURY provides tools for identifying network traffic congestion and peak usage times, and for making memory usage projections. The application data processing specialises in two tasks: the parametrisation of the memory usage of individual applications and the extraction of the seasonal component from network traffic data. AUGURY uses a different underlying assumption for each of these two tasks. With respect to the memory usage, a limited number of single-valued parameters are assumed to be sufficient to parameterize any application being hosted on the server. Regarding the network traffic data, long-term patterns, such as hourly or daily exist and are being induced by work-time schedules and automatised administrative jobs. In this paper, the implementation of each of the two tasks is presented, tested using locally-generated data, and applied to data from weather forecasting applications hosted on a web server. This data is used to demonstrate the insight that AUGURY can add to the monitoring of server and cloud infrastructures.

Comment: 8 pages, 9 figures, submitted to SYNASC2016

Original document

The different versions of the original document can be found in:

• http://arxiv.org/abs/1607.08344

• http://arxiv.org/pdf/1607.08344

• http://xplorestaging.ieee.org/ielx7/7827704/7829572/07829634.pdf?arnumber=7829634,

http://dx.doi.org/10.1109/synasc.2016.062

• https://dblp.uni-trier.de/db/journals/corr/corr1607.html#GutierrezW16,

http://ui.adsabs.harvard.edu/abs/2016arXiv160708344G/abstract,

https://arxiv.org/abs/1607.08344,

https://arxiv.org/pdf/1607.08344v1,

https://ru.arxiv.org/abs/1607.08344,

https://au.arxiv.org/pdf/1607.08344,

https://au.arxiv.org/abs/1607.08344,

https://ru.arxiv.org/pdf/1607.08344,

https://academic.microsoft.com/#/detail/2490430997