Splitting and placement of data-intensive applications with machine learning for power system in cloud computing

Aiming to meet the growing demands for observation and analysis in IoT-based (Internet of Things) power system, the machine learning technology is adopted sto deal with the data-intensive power electronic applications in IoT. By feeding previous power electronic data into the learning model, accurate information is drawn and the quality of IoT-based power services is improved. Generally, the data-intensive electronic applications with machine learning are split as numerous data/control constrained tasks by workflow technology. The efficient execution of this data-intensive Power Workflow (PW) needs massive computing resources, which are available in the cloud infrastructure. Nevertheless, the execution efficiency of PW decreases due to inappropriate sub-task and data placement. In addition, the power consumption explodes due to massive data acquisition. To address these challenges, a PW placement method named PWP is devised. Specifically, the Non-dominated Sorting Differential Evolution (NSDE) is used to generate placement strategies. The simulation experiments show that PWP achieves the best trade-off among data acquisition time, power consumption, load distribution and privacy preservation, confirming that PWP is effective for the placement problem.