Scapegoat: an Adaptive monitoring framework for Component-based systems


Modern component frameworks support continuous deployment and simultaneous execution of multiple software components on top of the same virtual machine. However, isolation between the various components is limited. A faulty version of any one of the software components can compromise the whole system by consuming all available resources. In this paper, we address the problem of efficiently identifying faulty software components running simultaneously in a single virtual machine. Current solutions that perform permanent and extensive monitoring to detect anomalies induce high overhead on the system, and can, by themselves, make the system unstable. In this paper we present an optimistic adaptive monitoring system to determine the faulty components of an application. Suspected components are finely instrumented for deeper analysis by the monitoring system, but only when required. Unsuspected components are left untouched and execute normally. Thus, we perform localized just-in-time monitoring that decreases the accumulated overhead of the monitoring system. We evaluate our approach against a state-of-the-art monitoring system and show that our technique correctly detects faulty components, while reducing overhead by an average of 80%.

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