Hierarchical Matrix Timestamps for Scalable UpdatePropagation

Update propagation is a technique for the weakly consistent replication of a database. Operations are performed at the local copy of the database, then are asynchronously propagated to the replicas. In large scale applications where the weak consistency can be tolerated (e.g., network routing tables, bulletin boards etc.), update propagation can give much better performance than conventional database techniques. However, standard algorithms for ensuring that an update is eventually propagated to all replicas require that each site store and communicate an O(N 2) matrix timestamp, where N is the number of sites that store a replica. If N is very large (e.g. N = 10; 000 or larger), then the cost of maintaining the matrix timestamp limits the scalability of the replicated database. Also, standard algorithms require global membership information, and keeping the membership information up-to-date can be diicult. In this paper, we present an algorithm that can signiicantly reduce the size of the matrix timestamp. The sites that store a replica of the database are partitioned among a set of domains. We replace matrix timestamps with hierarchical matrix timestamps, which have precise information about processors in the same domain, and summary information about other domains. If there are O(p N) domains, the hierarchical matrix timestamp can require only O(N) space. Furthermore, the hierarchical matrix timestamp allows hierarchical replica management. The cost of the reduced space of the hierarchical matrix timestamp is a lower quality estimate of the progress of the other processors. As a consequence, updates must be buuered for a longer time to ensure safety, increasing the size of the update log. We present detailed simulation studies that quantify this tradeoo. We explore a set of optimization techniques for reducing the average log size. When all of the optimizations are applied, there is no increase in the log size as compared to the regular update propagation algorithm.