Performance analysis of randomized algorithm for optimal query plan generation in distributed environment

The method of finding the optimal processing method to answer a query is called Query optimization, whereas a collection of various sites, distributed over a computer network is called Distributed database. In Distributed Database, the site communicates with each other through networks. The processing cost and the transmission cost are the important issues arise during evaluation of query cost. Several algorithms have been developed to find the best optimal solution for a particular query; however they all have their certain limitations. Hence, to find the optimal cost for a particular query is emerging as an open challenge for many researchers. Therefore the cost-based query optimization technique has emerged as an important concept for dealing with the query optimization. With the help of fragmentations one can replicate each fragment to various distributed sites, since the same data may be accessed from applications that executes at a number of sites. In this paper, the concepts of randomized algorithms have been explored. The two best know randomized algorithms are Simulated Annealing and Iterative Improvement, which have been implemented and their results have been compared on the number of optimal query plan generated and the average query processing cost. The attempts have been made to proposed and implemented the concept of two phase query optimization algorithm, which is also known as hybrid approach which works in two, phase: in first phase we apply iterative improvement algorithm followed by simulated annealing algorithm. The results of the experiments are also compared on the previous two factors i.e no. of query plan generated and average query processing cost by varying the number of relation and the no of distributed sites participating.