An iterative method for distributed database optimization

摘要

The development of a distributed database system requires effective solutions to many complex and interrelated design optimization problems. The cost dependencies between query optimization and data allocation on distributed systems are well recognized but little understood. We investigate these dependencies by proposing and analysing an iterative heuristic which provides an integrated solution to the query optimization and data allocation problems. The optimization heuristic iterates between finding minimum-cost query strategies and minimum-cost data allocations until a local minimum for the combined problem is found. A search from convergence efficiently scans the optimization search space for lower-cost solutions. In this paper, we apply the iterative heuristic to a realistic distributed database system model and a general class of queries and obtain very significant performance benefits. Experimental results demonstrate clear improvements in performance for the iterative method over existing design methods in a general-query environment. The iterative heuristic is proposed as a framework for future research extensions to achieve distributed system optimization.