Cost Minimization for Big Data Processing in Geo-Distributed Data Centres

Demand on big data is being increasing day by day and also increasing heavy burden on computation, storage and communication in data centres, which lead to considerable expenditure to data centre providers. So, cost minimization became an issue for the upcoming big data. One of the main feature of big data is coupling of data and computation as computation task. This can be done only when that corresponding is available for computation. Three tasks like data placement, task assignment and data movement influence the expense of data centres. In this paper we study how to minimize cost through joint optimization of these above three factors for big data service in geo distributes data centres. Here we propose 2-D Markov chain to describe time to complete a particular task with consideration of data transmission and computation to derive average task completion time in closed time. In addition, we here model the problem as mixed integer nonlinear programming and propose a solution to linearize it.