A Criticality-Aware Dynamic Task Scheduling Mechanism for Efficient Resource Load Balancing in Constrained Smart Manufacturing Environment

Abstract

Smart factory is an exemplification of the Industrial Internet of Things (IIoT), connecting devices to expedite the production process and delivery of customized products. Today’s intelligent manufacturing systems strive to develop low cost product with robust manufacturing process to compete the global market challenges. Though the quest to provide robust, reliable, adaptive, proactive, and real-time services to smart factory production processes has got little attention. To this aim, establishment of a robust solution for efficient resource utilization, load balancing and task scheduling has become a de-facto necessity. This paper presents an enhanced learning assisted task scheduling mechanism based on task Criticality and Collapse Aware Scheduling (CCAS) algorithm. The proposed mechanism is developed using two modules; namely task scheduling mechanism based on task criticality and collapse aware strategy, and an ensemble prediction model i.e., Gradient Boosting Decision Tree (GBDT) to proactively predict the machine utilization and task safe execution status. The proposed ensemble learning framework provides high level feature abstraction by learning the task parameters to predict task status and machine utilization. Furthermore, an intelligent scheduling mechanism is developed for optimal resource allocation to maximize the production in constrained smart manufacturing machine’s network. Extensive experimentation and comparative analysis with the conventional Rate Monotonic (RM) algorithm has been carried out to validate the performance of the proposed approach. The results demonstrate that the proposed enhanced scheduling mechanism yields superior performance in terms of response time, task dropout and starvation rates compared to the conventional RM method. The developed predictive CCAS scheduling reduced response time, task dropout and starvation rate by 13%, 15%, and 14% respectively, compared to the baseline RM scheduling approach. The results shows that the CCAS shall enhance the resource utilization in smart factory yielding enhanced productivity and reduced cost of production.

Keywords

• Industrial Internet of Things
• smart manufacturing
• embedded IoT
• real-time task scheduling