🤖 AI Summary
This work addresses the challenge of simultaneously achieving low latency, high energy efficiency, high reliability, and adherence to multiple resource constraints in edge-hub-cloud collaborative IoT workflow scheduling. To this end, it proposes the first exact multi-objective, multi-constraint joint optimization model based on Continuous-Time Mixed-Integer Linear Programming (CT-MILP). By explicitly modeling task dependencies and incorporating a selective task replication mechanism, the model enhances system reliability while minimizing redundant overhead. Evaluated on both real-world and synthetic workflows, the proposed approach outperforms state-of-the-art heuristic algorithms, reducing average latency by 29.83% and energy consumption by 33.96%, while improving reliability by 28.49%. The method demonstrates practical runtime efficiency and favorable scalability, making it suitable for complex IoT environments.
📝 Abstract
Emerging IoT-enabled cyber-physical applications demand low-latency, energy-efficient, and reliable execution across resource-constrained edge devices with heterogeneous multicore processors and diverse sensing and actuating capabilities, in collaboration with a hub device and a cloud server. These workflow-based applications comprise interdependent tasks that must be executed under stringent deadline, reliability, capability, memory, storage, and energy constraints. Given their critical nature, exact optimization is necessary to obtain optimal schedules that ensure dependable operation. Existing scheduling approaches, both exact and heuristic, fail to jointly address all these objectives and constraints. To this end, we propose an exact multi-objective and multi-constrained workflow scheduling approach for edge-hub-cloud cyber-physical systems, based on continuous-time mixed integer linear programming. The proposed formulation jointly optimizes latency, energy, and reliability, while holistically addressing timing and resource constraints. To enhance reliability while avoiding the overhead of unnecessary task replicas, it selectively employs task duplication. We evaluate our approach against a widely used heuristic, which we extend to ensure a fair and meaningful comparison, using a real-world IoT workflow and synthetic task graphs of varying sizes, across different system configurations and objective trade-offs. The proposed method consistently outperforms the heuristic, achieving up to 29.83%, 33.96%, and 28.49% average improvements in latency, energy, and reliability, respectively, while attaining practical runtimes. Overall, the experimental results demonstrate the effectiveness of our approach under various system configurations and objective trade-offs, and show its practical scalability to task graphs of sizes relevant to the targeted applications and system architecture.