Technical Perspective: Balancing At All Loads

Large-scale distributed parallel computing has become necessary for handling machine learning and other algorithms with ever-increasing complexity and data requirements. For example, Google TensorFlow can execute distributed algorithms that require thousands of computing nodes to work simultaneously. However, computing systems suffer from random fluctuations in service times. Power management, software or hardware failures, maintenance, and resource sharing are the primary causes of service time variability. Failures and maintenance are inevitable, and power management is crucial for reducing energy consumption.