Mutation-based greybox fuzzing---unquestionably the most widely-used fuzzing technique---relies on a set of non-crashing seed inputs (a corpus) to bootstrap the bug-finding process. When evaluating a fuzzer, common approaches for constructing this corpus include: (i) using an empty file; (ii) using a single seed representative of the target's input format; or (iii) collecting a large number of seeds (e.g., by crawling the Internet). Little thought is given to how this seed choice affects the fuzzing process, and there is no consensus on which approach is best (or even if a best approach exists).

The motivation for this paper is to extend the state of the art in distributed autonomics praxis as applied to telecommunications network management. We describe how the task assignment problem, common in telecommunications service activation workflow processes, can be effectively solved through the use of distributed autonomic auction agents. In doing so, we present our telecommunications service architecture that locates autonomic mechanisms. We describe a formal framework for the autonomic mechanism based on Holland's adaptation theory. We then present the results of an experimental simulation study that shows that the autonomic mechanism provides near optimal performance for the problem, whilst conferring significant advantages over other common implementations in terms of robustness, scalability and disaster recovery. We conclude that autonomic mechanisms confer significant advantages to TMN, but it does so by hiding complexity rather than simplifying the system operations as has sometimes been reported.