Optimization strategies through big-data migration in distributed cloud databases

Big data term tends towards production of data at bulk level from different institutes communicate to share information using enormous medias as source of connections between scattered branches. That process might produce data of symmetric or different in nature and stored on cloud or local data store of organization. Decrement in response time to fetch data efficiently, feedback in form of correct and concurrence data are major expected quality parameters try to maintain in development process of cloud database distributions. Different methodologies introduced in Query optimization process for performance up gradation of cloud transactions. Communication systems are collections of different resources, shared in between local or global points, designed implemented in optimal architecture framework that used for business transactions and data migration process. Such designs might be implemented on hardware level, software level and blend of both as per nature of user requirement specifications. IN-memory grid, large cache memory storage space, used to enhance availability of same data requested by institutional websites for users those request. Another approach (Evolutionary algorithm) is management of resources used for optimization of data migration networks that reduce overall cost of hardware connected for completion of tasks carried out in query processing. Research work is effort towards comparisons of different strategies used in optimization domain of distributed cloud databases. Experimental work perform on integrated system, use In-memory data grid for similar data availability enhancement and evolutionary algorithm for resource allocation to particular query plan. Results depicts that over all new system is slightly better than each ones according time and cost parameters. Future work for new lines also discussed. Proposed system's pros and cons declared for refinement of developed methodology.