An Improved Metadata Model for Big Data Processing in Cloud Data Centers

Data centers require advanced models to study confrontation of big data. We have developed an improved consistent model that processes the incoming tasks in minimal time and in an efficient manner considering two problem statements for this paper: (1) Portfolio pricing and (2) Counting words in dictionary. The exclusiveness of the idea is not to submit the task data with the task. We keep all data separately in a data grid (GD) domain. The main component in our study for this task, job dispatcher (JD), divides the input job in an efficient manner and distributes them to computing grid (CG) nodes, which are responsible for processing the tasks, and sending the result status back to JD and putting the results in the data grid. The job dispatcher aggregates these results.