- Gian Ntzik
POSIX is a standard for operating systems, with a substantial part devoted to specifying file-system operations. File-system operations exhibit complex concurrent behaviour, comprising multiple actions affecting different parts of the state: typically, multiple atomic reads followed by an atomic update. However, the standard’s description of concurrent behaviour is unsatisfactory: it is fragmented; contains ambiguities; and is generally under-specified. We provide a formal concurrent specification of POSIX file systems and demonstrate scalable reasoning for clients. Our speciation is based on a concurrent specification language, which uses a modern concurrent separation logic for reasoning about abstract atomic operations, and an associated refinement calculus. Our reasoning about clients highlights an important difference between reasoning about modules built over a heap, where the interference on the shared state is restricted to the operations of the module, and modules built over a file system, where the interference cannot be restricted as the file system is a public namespace. We introduce specifications conditional on context invariants used to restrict the interference, and apply our reasoning to lock files and named pipes. Program reasoning based on separation logic has been successful at verifying that programs do not crash due to illegal use of resources, such invalid memory accesses. The underlying assumption of separation logics, however, is that machines do not fail. In practice, machines can fail unpredictably for various reasons, such as power loss, corrupting resources or resulting in permanent data loss. Critical software, such as file systems and databases, employ recovery methods to mitigate these effects. We introduce an extension of the Views framework to reason about programs in the presence of such events and their associated recovery methods. We use concurrent separation logic as an instance of the framework to illustrate our reasoning, and explore programs using write-ahead logging, such a stylised ARIES recovery algorithm.
Ph.D. Thesis, Imperial College London