Scalable Self-Healing Systems

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Enterprise information systems today comprise large, complex interconnected assemblages of software, computers, networks, etc. These systems are mission critical, and as such are expect to be highly available, offer good performance, and operate within constrained budgets. Currently, this is achieved by having highly available datacenter administrators actively monitor critical services, identifying failures, service-level agreement (SLA) violations, etc., localizing and diagnosing faulty components, and generally ensuring that the system is in a correctly functioning state. This approach is expensive (the cost to business in the US is approximately $100 billion annually, rising 30% by 2018; US Bureau of Labor Statistics). While there has been recent research and industry attention given to having such systems self-heal, current techniques require the software components to be simple, stateless, and crash-only. These requirements are hard to attain with legacy enterprise information systems, Furthermore, current approaches consider only a small set of very generic automated recovery actions. To address these limitations, accurate fault localization and diagnosis methods, together with sophisticated error-recovery algorithms, are required. It is the objective of this research to devise such novel algorithms and methods, and to integrate them within a systems-management infrastructure, so as to create a self-healing meta-server which will provide end-to-end error detection, fault localization and diagnosis, and error recovery, and to do so at the datacenter scale. Our approach is to create scalable, run-time behavioural models of the normal operation of the system, and to identify deviations between system behaviour and run-time-model prediction. Any such anomalies are typically caused by workload change, system change, or system fault; we can monitor workload and system change, and so neither of those are identified, a fault is presumed, and recovery initiated. Given the large costs of running enterprise information systems, our research is expected to be very significant in cost reduction.