QPSO Based Load Balancing Mechanism In Cloud Environment

We introducing a new algorithm called Quantum particle swarm optimization is a new Particle swarm optimization variant, which outperforms the particle swarm optimization search but few control parameters are there. The weighted attractors of new particle in QPSO algorithm are generated around the global best particle and previous best particles. The main reason for result in attracting faster. The strategy behind global search and local search to balance the exploration and exploitation. A Large amount of application data are stored in distributed data center in scientific workflow. A data manager will occupy smartly which are selected by data center in these data center. However, case of information are not have a location for hard and fast. A Challenge will be done in one task that need several data set located in large volumes of data while they are moving and located in different data center. Here we using four modules like data splitting, location awareness, node localization, load balancing to move the data's more efficiently. QPSO algorithm considers data dependencies in cloud environment and data- intensive workflow features. The workflows use for data placement that can decrease the data transmission between data center effectively and also reduced the number of data movement.