In distributed database systems, tables are frequently fragmented and replicated over a number of sites in order to reduce network communication costs. How to fragment, when to replicate and how to allocate the fragments to the sites are challenging problems that has previously been solved either by static fragmentation, replication and allocation, or based on a priori query analysis. Many emerging applications of distributed database systems generate very dynamic workloads with frequent changes in access patterns from different sites. In such contexts, continuous refragmentation and reallocation can significantly improve performance. In this paper we present DYFRAM, a decentralized approach for dynamic table fragmentation and allocation in distributed database systems based on observation of the access patterns of sites to tables. The approach performs fragmentation, replication, and reallocation based on recent access history, aiming at maximizing the number of local accesses compared to accesses from remote sites. We show through simulations and experiments on the DASCOSA distributed database system that the approach significantly reduces communication costs for typical access patterns, thus demonstrating the feasibility of our approach.

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Semantic caching augments cached data with a semantic description of the data. These semantic descriptions can be used to improve execution time for similar queries by retrieving some data from cache and issuing a remainder query for the rest. This is an improvement over traditional page caching, since caches are no longer limited to only base tables but are extended to contain intermediate results. In large-scale distributed database systems, using a central server with complete knowledge of the system will be a serious bottleneck and single point of failure. In this paper, we propose a distributed semantic caching method where sites make autonomous caching decisions based on locally available information, thereby reducing the need for centralized control. We implement the method in the DASCOSA-DB distributed database system prototype and use this implementation to do experiments that show the applicability and efficiency of our approach. Our evaluation shows that execution times for queries with similar subqueries are significantly reduced and that overhead caused by cache management is marginal.

Peer-to-peer database systems (P2PDBs) aim at providing database services with node autonomy, high availability and loose coupling between participating nodes by building the DBMS on top of a peer-to-peer network. A key feature of current peer-to-peer systems is resilience to churn in the overlay network layer. A major challenge in P2PDBs is to provide similar robustness in the data and query processing layer. In this paper we in particular describe how aggregation queries in P2PDBs can be handled in order to reduce the impact of churn on accuracy of results. We perform a formal study of data loss and accuracy of such queries, and describe new approaches that increase the accuracy of aggregation queries in P2PDBs under churn.