Performance Control for Nonlinear Hadoop-Mapreduce Computing Systems

Abstract

This paper addresses the issue of model establishment and performance control for nonlinear computing systems based on Hadoop-Mapreduce platform. The parameter mapred.map.tasks is selected as the control input data and the CPU usage is selected as the output data. By using least-squares principle, a Hammerstein model for this relationship is identified and validated. The control law consists of a nonlinear compensator and a PID feedback. By testing in Simulink, the Hammerstein model is verified appropriate and the designed nonlinear control drives the CPU usage to achieve the target value asymptotically.

Keywords:

• Hadoop
• mapreduce
• computing
• control
• Hemmerstein

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61364012) and the Natural Science Foundation of Shandong Province (Grant No. ZR2019MF052).