A Threshold Based Dynamic Data Allocation Algorithm-A Markov Chain Model Approach

In this study, a new dynamic data allocation algoritlnn for non-replicated Distributed Database Systems (DDS), name1y tbe ıbresho1d a1goritbm, is formulated and proposed. The ıbresho1d a1goritbm reallocates data with respect to changing data access patterns. The proposed algoritlnn İs distributed İn the sense that each node autonomously decides whether to transfer the ownership of a fragınent İn DDS to another node or not. The transfer decİsİon depends on the past access es of the fragınent. Each fragınent continuously migrates from the node where it İs not access ed locaııy more than a certaİn number of past accesses, namely a threshold value. The threshold algoritlnn İs modeled for a fragınent of the clatabase as a finite Markov chain with constant node access probabilities. In the model, a special case, where aıı nodes have equal access probabilities except one with a different access probability, is analyzed. it has been shown that for positive threshold values the fragment wiıı tend to remain at the node with the higher access probability. it is also sho"\iVIl that the greater the threshold values are, the greater the tendency of the fragment to remain at the node with higher access probability wiıı be. The threshold algoritlnn is especiaııy suitable for a DDS where data access pattem changes dynamicaııy.