Monitoring is widely applied in problem diagnosis, fault localization, and system maintenance. And since the cloud infrastructure is complex, the applications on the cloud are therefore complex, which makes monitoring in cloud more difficult. Rich monitors that contain composite and heterogeneous probes are often used in service-oriented system monitoring. These rich monitors often involve multiple entities, and the interpretation may require expert opinions from multiple domains. This paper proposes a knowledge-based collaborative monitoring approach to find out minimal cost monitor deployment in a cloud environment. The approach contains two main phases. In the knowledge acquisition phase, three acquisition tables, monitor-probe relationship matrix, cost of monitoring, and probe-problem dependence matrix, are generated according to diagnosis ontology and monitor ontology acquired from domain experts. And then based upon the three acquisition tables and three consensus building strategies, we formulate the problem of optimizing the cost of monitoring as an Integer Linear Programming (ILP) problem, which is NP-Complete. In the monitor deployment phase, the proposed algorithm applies two heuristic rules to address the problem. Three experiments are conducted to evaluate the performance of the proposed approach. The results from the experiments show that our approach is effective and produce quality approximate solutions in monitor deployment.
Minimal Cost Flow in Processing Networks, a Primal Approach.
