Incremental garbage collection for causal relationship computation in distributed systems

Many distributed applications require the knowledge of the causality relation induced by the computation. Reconstructing this relation appears to be an interesting tool for such applications, but a vector of size S - where S is the number of processes - must be attached to each event to achieve this reconstruction. This induces a large overhead in secondary memory. After defining special events of the computation - some kind of checkpoints - we propose two algorithms that discard unnecessary data for the causal relationship reconstruction. The first algorithm acts on-the-fly while the second acts during reconstruction