Search papers, labs, and topics across Lattice.
This paper introduces a continuous-time mixed integer linear programming (MILP) formulation for exact multiobjective and multiconstrained workflow scheduling in edge-hub-cloud cyber-physical systems. The formulation jointly optimizes latency, energy, and reliability under stringent timing and resource constraints, using selective task duplication to enhance reliability. Experiments on real-world IoT workflows and synthetic task graphs demonstrate that the proposed method outperforms a heuristic approach, achieving significant improvements in latency, energy, and reliability (up to 29.83%, 33.96%, and 28.49%, respectively) while maintaining practical runtimes.
Ditch the heuristics: MILP delivers up to 30% better latency, energy, and reliability for IoT workflow scheduling in edge-hub-cloud systems.
Emerging Internet of Things (IoT)-enabled cyber鈥損hysical applications, such as autonomous critical infrastructure inspection, 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 multiobjective and multiconstrained workflow scheduling approach for edge鈥揾ub鈥揷loud cyber鈥損hysical systems (CPSs), based on continuous-time mixed integer linear programming (MILP). 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 (TGs) of varying sizes, across different system configurations and objective tradeoffs. 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 tradeoffs, and show its practical scalability to TGs of sizes relevant to the targeted applications and system architecture.
