Reducing Communication Overhead in Threshold Monitoring with Arithmetic Aggregation

With increasing adoption of distributed systems, monitoring has become an important research topic in recent years. Monitoring itself introduces overhead to the system caused by communication for collecting measurement data between monitoring nodes and measuring nodes. Reducing communication frequency of monitoring is very significant, especially for threshold monitoring which only cares about whether some metric crosses certain threshold or not. Currently threshold monitoring only considers simple aggregation values such as sum or average of values of measurement data. However, in this paper, we take a further exploration to arithmetic aggregation which is more complicated arithmetic result of measurement data, not just the sum or average of them. We present an approach to solve the communication overhead problem of arithmetic aggregation in threshold monitoring, where corresponding algorithms are designed. In our approach, the global threshold can be split into many local thresholds which can be set remotely on distributed local monitoring nodes. Communication will only take place when the locally observed numerical value of measurement exceeds corresponding local thresholds. We argue that communications will be reduced significantly based on the assumption that numerical values of monitored measurements would not oscillate widely or rapidly all the time. By conducting experiments based on data from real scenes, we can demonstrate that our solutions outperform the traditional approaches.