An Energy-Aware Multistages Hybrid Scheduling Approach for IoT Workflow Applications With Reliability Constraint in Cloud Computing Systems
Lingjuan Ye, Liwen Yang, Xinchao Zhao, Yuanqing Xia · IEEE Internet of Things Journal · 2025
With the rapid advancement of cloud computing, cloud services have been widely adopted for managing large-scale and complex IoT workflow applications due to their robust computational capabilities. However, efficiently scheduling and deploying these workflows while ensuring quality-of-service (QoS) for diverse users remains a significant challenge for cloud service providers. In this study, we propose a novel multi-stage workflow scheduling algorithm (RE-ACO) for energy-efficient management of reliability-constrained IoT applications in cloud environments. The algorithm operates in three key stages: Task ordering by ACO, reliability constraint distribution with feedback information and energy-aware task assignment. The RE-ACO leverages ACO and an energy-aware task assignment strategy to optimize energy usage without compromising workflow reliability. First, the ACO algorithm determines the optimal task execution sequence. Next, a feedback-based reliability distribution method dynamically assigns sub-reliability constraints to individual tasks. Finally, each task is allocated to a virtual machine (VM) that minimizes energy consumption while meeting its sub-reliability requirement. Simulation results demonstrate that RE-ACO outperforms existing approaches, achieving the lowest energy consumption for reliability-constrained workflow scheduling compared to three benchmark algorithms.