High-availability of software systems requires automated handling of crashes in presence of errors. Failure-oblivious computing is one technique that aims to achieve high availability. We note that failure-obliviousness has not been studied in depth yet, and there is very few study that helps understand why failure-oblivious techniques work. In order to make failure-oblivious computing to have an impact in practice, we need to deeply understand failure-oblivious behaviors in software. In this paper, we study, design and perform an experiment that analyzes the size and the diversity of the failure-oblivious behaviors. Our experiment consists of exhaustively computing the search space of 16 field failures of large-scale open-source Java software. The outcome of this experiment is a much better understanding of what really happens when failure-oblivious computing is used, and this opens new promising research directions.
Software systems are constantly evolving, with new versions and patches being released on a continuous basis. Unfortunately, software updates present a high risk, with many releases introducing new bugs and security vulnerabilities. We tackle this problem using a simple but effective multi-version based approach. Whenever a new update becomes available, instead of upgrading the software to the new version, we run the new version in parallel with the old one; by carefully coordinating their executions and selecting the behaviour of the more reliable version when they diverge, we create a more secure and dependable multi-version application. We implemented this technique in Mx, a system targeting Linux applications running on multi-core processors, and show that it can be applied successfully to several real applications such as Coreutils, a set of user-level UNIX applications; Lighttpd, a popular web server used by several high-traffic websites such as Wikipedia and YouTube; and Redis, an advanced key-value data structure server used by many well-known services such as GitHub and Flickr.