%0 Conference Proceedings
%A Mormul, Mathias; Hirmer, Pascal; Stach, Christoph & Mitschang, Bernhard
%D 2020
%T DEAR: Distributed Evaluation of Alerting Rules
%E Khan, Latifur & Huang, Gang
%B Proceedings of the IEEE 13th International Conference on Cloud Computing
%C Beijing
%I IEEE
%P 158-165
%S CLOUD '20
%8 October
%@ 978-1-7281-8780-8
%3 inproceedings
%F cloud_20_dear
%K cloud monitoring; agent-based; alerting
%X Cloud computing passed the hype cycle long ago and firmly established itself as a future technology since then. However, to utilize the cloud as cost-efficiently as possible, a continuous monitoring is key to prevent an over- or under-commissioning of resources. In large-scaled scenarios, several challenges for cloud monitoring, such as high network traffic volume, low accuracy of monitoring data, and high time-to-insight, require new approaches in IT Operations while considering administrative complexity. To handle these challenges, we present DEAR, the Distributed Evaluation of Alerting Rules. DEAR is a plugin for monitoring systems which automatically distributes alerting rules to the monitored resources to solve the trade-off between high accuracy and low network traffic volume without administrative overhead. We evaluate our approach against requirements of today's IT monitoring and compare it to conventional agent-based monitoring approaches.
%R 10.1109/CLOUD49709.2020.00034