Iterative-improvement-based declustering heuristics for multi-disk databases

  • Authors:

  • Mehmet Koyutürk;Cevdet Aykanat

  • Affiliations:

  • Department of Computer Sciences, Purdue University, West Lafayette, IN;Computer Engineering Department, Bilkent University, Ankara 06800, Turkey

  • Venue:
  • Information Systems
  • Year:
  • 2005

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Abstract

Data declustering is an important issue for reducing query response times in multi-disk database systems. In this paper, we propose a declustering method that utilizes the available information on query distribution, data distribution, data-item sizes, and disk capacity constraints. The proposed method exploits the natural correspondence between a data set with a given query distribution and a hypergraph. We define an objective function that exactly represents the aggregate parallel query-response time for the declustering problem and adapt the iterative-improvement-based heuristics successfully used in hypergraph partitioning to this objective function. We propose a two-phase algorithm that first obtains an initial K-way declustering by recursively bipartitioning the data set, then applies multiway refinement on this declustering. We provide effective gain models and efficient implementation schemes for both phases. The experimental results on a wide range of realistic data sets show that the proposed method provides a significant performance improvement compared with the state-of-the-art declustering strategy based on similarity-graph partitioning.