How much management is management enough? Providing monitoring processes with online adaptation and learning capability

Recent investigations of management traffic patterns in production networks suggest that just a small and static set of management data tends to be used, the flow of management data is relatively constant, and the operations in use for manager-agent communication are reduced to a few, sometimes obsolete set. This is an indication of lack of progress of monitoring processes, taking into account their strategic role and potential, for example, to anticipate and prevent faults, performance bottlenecks, and security problems. One of the main reasons for such limitation relies on the fact that operators, who still are a fundamental element of the monitoring control loop, can no longer handle the rapidly increasing size and heterogeneity of both hardware and software components that comprise modern networked computing systems. This form of human-in-the-loop management certainly hampers timely adaptation of monitoring processes. To tackle this issue, this paper presents a model, inspired by the reinforcement learning theory, for adaptive network, service and application monitoring. The model is instantiated through a prototypical implementation of an autonomic element, which, based on historical and even unexpected values retrieved for management objects, dynamically widens or restricts the set of management objects to be monitored.