Parallel Scientific Power Calculations in Cloud Data Center Based On Decomposition-Coordination Directed Acyclic Graph

Abstract

With the expansion scale of interconnected power systems and refined state perception, scientific power calculations become more complex and diverse. They need faster computation speed and better scalability to support power flow calculation, reactive power optimization, and static/transient stability analysis for unit scheduling. Therefore, this paper proposes a novel cloud data center task mapping algorithm of the Stoer-Wagner binary tree (SWBT) to support accelerated executions of these calculations. Firstly, based on the block bordered-diagonal form of the admittance matrix, high-time complexity scientific power calculations are transformed into a unified multi-task decomposition-coordination directed acyclic graph (DC-DAG). And then, the critical tasks in this DC-DAG are found and the virtual machines encapsulating them are matched with physical machines in the data center preferentially. Finally, on CloudSim, a cloud computing platform, the multi-job mixed experiments of 118-13659 bus power systems are carried out. In addition, real-time workload performance is enhanced in two very large real-world power systems. Studies illustrate that SWBT can improve the underlying physical machine resource utilization and reduce data interaction transmission hops to achieve better computing acceleration performance.