Cost-aware optimal data allocations for multiple dimensional heterogeneous memories using dynamic programming in big data

Multiple constraints in SPMs are considered a problem that can be solved in a nondeterministic polynomial time. In this paper, we propose a novel approach solving the data allocations in multiple dimensional constraints. For supporting the approach, we develop a novel algorithm that is designed to solve the data allocations under multiple constraints in a polynomial time. Our proposed approach is a novel scheme of minimizing the total costs when executing SPM under multiple dimensional constraints. Our experimental evaluations have proved the adaptation of the proposed model that could be an efficient approach of solving data allocation problems for SPMs.