NIRVANA: A Non-intrusive Black-Box Monitoring Framework for Rack-Level Fault Detection

Many organizations today still manage mid or large in-house data centers that require very expensive maintenance efforts, including fault detection. Common monitoring frameworks used to quickly detect faults are complex to deploy/maintain, expensive, and intrusive as they require the installation of probes on monitored hw/sw to collect raw data. Such intrusiveness can be problematic as it imposes installation/management overhead and may interfere with security/privacy policies. In this paper we introduce NIRVANA, a novel monitoring system for fault detection that works at rack-level and is (i) non-intrusive, i.e., it does not require the installation of software probes on the hosts to be monitored and (ii) black-box, i.e., agnostic with respect to monitored applications. At the core of our solution lies the observation that aggregated features that can be monitored at rack-level in a non-intrusive and black-box way, show predictable behaviors while the system works in both fault-free and faulty states, it is therefore possible to detect and identify faults by monitoring and analyzing any perturbations to these behaviors. An extensive experimental evaluation shows that non-intrusiveness does not significantly hamper the fault detection capabilities of the monitoring system, thus validating our approach.