Implementing Large-Scale Autonomic Server Monitoring Using Process Query Systems

In this paper we present a new server monitoring method based on a new and powerful approach to dynamic data analysis: process query systems (PQS). PQS enables user-space monitoring of servers and, by using advanced behavioral models, makes accurate and fast decisions regarding server and service state. Data to support state estimation come from multiple sensor feeds located within a server network. By post-processing a system´s state estimates, it becomes possible to identify, isolate and/or restart anomalous systems, thus avoiding cross-infection or prolonging performance degradation. The PQS system we use is a generic process detection software platform. It builds on the wide variety of system-level information that past autonomic computing research has studied by implementing a highly flexible, scalable and efficient process-based analytic engine for turning raw system information into actionable system and service state estimates