An Adaptive Cloud Monitoring Framework Based on Sampling Frequency Adjusting

Abstract

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.