Bi-Objective Optimization for Industrial Robotics Workflow Resource Allocation in an Edge–Cloud Environment

The application scenarios and market shares of industrial robots have been increasing in recent years, with them comes a huge technical demand for robot-monitoring system (IRMS). With the development IoT cloud computing technologies, robot monitoring has entered era. However, data tasks characteristics large volume high information redundancy, need to occupy amount communication bandwidth architecture, so cloud-based IRMS gradually become unable meet its performance cost requirements. Therefore, this work constructs edge–cloud architecture IRMS. task will be executed form workflow local monitor allocate resources subtasks by analyzing current situation network. In work, allocation problem is modeled as latency bi-objective optimization problem, solution based on evolutionary algorithm heuristic improvement NSGA-II. experimental results demonstrate that proposed can find non-dominated solutions faster closer Pareto frontier problem. select an effective needs task.