Runtime Behavioural Models for Dependable Systems

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Modern computing comprises data centers, client devices, and the Internet connecting them. With millions of servers, billions of client devices, and numerous networks, all under different administrative control, achieving dependable computing is difficult. Even if both hardware and software are highly reliable, with so many applications, at any given time numerous of them will have failed. Data-center dependability is achieved by replication and untime software management implementing Recovery-oriented Computing (RoC). Network dependability is achieved via redundancy of network interfaces and interconnections. Achieving dependable clients is more difficult, as they do not have redundancy. They run multiple applications, and so generic recovery actions used in data-center RoC do not work. Currently clients record device and application log data and send it to a data center for analysis. The problem with this is that log records tend to be whatever a developer thought might help in debugging an application which is often not useful for a user or an operator; determining appropriate recovery actions based on log records is hard; worse, the developer may have missed critical log points; finally, the amount of log data sent for analysis is vast and growing, with companies gathering terabytes of log records daily. We plan to extend our work in system monitoring and error detection. Currently we model the run-time behaviour of software systems using a mixture of regression analysis, information-theoretic models, and clustering based on textual similarity of logs messages. We compare monitored behaviour with our models; deviation from the model indicates workload change, software change, or error in the system. When we can eliminate the first two, the can infer error. In this research we will extend our range of run-time models to incorporate the effects of proposed self-recovery actions on system behaviour. Current tools address errors by applying a limited sequence of automated options. Most software systems have numerous recovery alternatives for different situations, as is evident in the knowledge databases maintained by support organizations of various software vendors. When such alternatives are available, fault localization and diagnosis using our modeling approach is likely critical, as a fixed sequence of recovery options will be ineffective and costly. The set of possible recovery actions will be extracted from known-successful techniques from support knowledge databases. After applying any recovery action, we expect to closely monitor system behaviour to determine the success or failure of the action, and adjust as necessary. In addition to creating a runtime management system for clients, we expect to develop automated logpoint insertion techniques to address the poor quality of log records as viewed from the operator and user perspective.