Paper Info
Title
Correlation-Model-Based Reduction of Monitoring Data in Data Centers.
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.
Year
DOI
Venue
2016
10.5220/0005794803950405
SMARTGREENS
Keywords
Field
DocType
Monitoring Data,Data Reduction,Time-series Prediction,Data Center
Data mining,Time series,Data modeling,Data collection,Computer science,Workload,Sampling (signal processing),Complex data type,Data center,Data reduction
Conference
ISBN
Citations 
PageRank 
978-989-758-184-7
2
0.41
References 
Authors
7
2
Name
Order
Citations
PageRank
Xuesong Peng121.77
Barbara Pernici23401488.75