Correlation-Model-Based Reduction of Monitoring Data in Data Centers

Xuesong Peng

Abstract

Nowadays, in order to observe and control data centers in an optimized way, people collect a variety of monitoring data continuously. Along with the rapid growth of data centers, the increasing size of monitoring data will become an inevitable problem in the future. This paper proposes a correlation-based reduction method for streaming data that derives quantitative formulas between correlated indicators, and reduces the sampling rate of some indicators by replacing them with formulas predictions. This approach also revises formulas through iterations of reduction process to find an adaptive solution in dynamic environments of data centers. One highlight of this work is the ability to work on upstream side, i.e., it can reduce volume requirements for data collection of monitoring systems. This work also carried out simulated experiments, showing that our approach is capable of data reduction under typical workload patterns and in complex data centers.

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Paper Citation


in Harvard Style

Peng X. and Pernici B. (2016). Correlation-Model-Based Reduction of Monitoring Data in Data Centers . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 395-405. DOI: 10.5220/0005794803950405


in Bibtex Style

@conference{smartgreens16,
author={Xuesong Peng and Barbara Pernici},
title={Correlation-Model-Based Reduction of Monitoring Data in Data Centers},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={395-405},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005794803950405},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Correlation-Model-Based Reduction of Monitoring Data in Data Centers
SN - 978-989-758-184-7
AU - Peng X.
AU - Pernici B.
PY - 2016
SP - 395
EP - 405
DO - 10.5220/0005794803950405