A Zero-Shot Learning Approach for Task Allocation Optimization in Cyber-Physical Systems

Abstract

The design and reorganization of Cyber-Physical Systems (CPSs) faces challenges due to the growing number of interconnected devices. To effectively handle disruptions and improve performance, rapid CPS design and development is crucial. The Task Resources Estimator and Allocation Optimizer (TREAO) addresses these challenges, by simulating and optimizing the tasks assignment to the CPS machines, recommending suitable software layouts for the CPS characteristics. It employs Zero-Shot Learning (ZSL) to predict task requirements in heterogeneous devices, enabling the characterization of software pipeline execution in distributed systems. The Genetic Algorithm (GA) component then optimizes the task assignment across available machines. Through experiments, the tool is evaluated for task characterization, CPS modeling and optimization performance. TREAO, when compared with similar tools, allows the simulation of more resource usage metrics (CPU, RAM, processing time and network delay) and increases flexibility in heterogeneous CPSs by predicting the task execution behavior and optimizing the task assignment.