An Adaptive Cloud Monitoring Framework Based on Sampling Frequency Adjusting

In a cloud platform, the monitoring service has become a necessary infrastructure to manage resources and deliver desirable quality-of-service (QoS). Although many monitoring solutions have been proposed in recent years, how to mitigate the overhead of monitoring service is still an opening issue. T...

Full description

Saved in:
Bibliographic Details
Published in:International journal of e-collaboration 2020-04, Vol.16 (2), p.12-26
Main Authors: Liu, Dongbo, Liu, Zhichao
Format: Article
Language:English
Subjects:
Accuracy
Adaptive sampling
Algorithms
Analysis
Cloud computing
Collaboration
Efficiency
Fault tolerance
Middleware
Monitoring
Monitoring systems
Prediction models
Sensors
Software
Topology optimization
Traffic models
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In a cloud platform, the monitoring service has become a necessary infrastructure to manage resources and deliver desirable quality-of-service (QoS). Although many monitoring solutions have been proposed in recent years, how to mitigate the overhead of monitoring service is still an opening issue. This article presents an adaptive monitoring framework, in which a traffic prediction model is introduced to estimate short-term traffic overhead. Based on this prediction model, a novel algorithm is proposed to dynamically change the sampling frequency of sensors so as to achieve better tradeoffs between monitoring accuracy and overhead. Also, a monitoring topology optimization mechanism is incorporated which enables to make more cost-effective decisions on monitoring management. The proposed framework is tested in a realistic cloud and the results indicate that it can significantly reduce the communication overhead when performing monitoring tasks for multiple tenants.
ISSN:1548-3673
1548-3681
DOI:10.4018/IJeC.2020040102