Cache Based Query Optimization Approach in Distributed Database

Query optimization in distributed databases is explicitly needed in many aspects of the optimization process, often making it imperative for the optimizer to consult underlying data sources while doing cost based optimization. This not only increases the cost of optimization, but also affects the trade-offs involved in the optimization process significantly. The leading cost in this optimization process is the cost of costing that traditionally has been considered insignificant. The optimizer can only afford a few rounds of messages to the under-lying data sources and hence the optimization techniques in this environment must be geared toward gathering all the required cost information with minimal communication. In this paper, a cache based query optimization model has been proposed which shows better hit ratio even for the initial queries made since local cache has been used instead of global cache. A cache is implanted between the local optimizer and local database. Whenever a query is given to a local optimizer, local optimizer first checks the cache rather than fetching the data directly from the database. In case, if the solution of query can be obtained from the cache, it results in saving a huge amount of computation time as accessing a cache is faster than accessing the database. The proposed cost optimization model works on the basis of four different factors i.e. server distance, server capacity, server load and current queue length to provide optimal node where query should be executed.