Exact, Efficient, and Reliable Multi-Objective and Multi-Constrained IoT Workflow Scheduling in Edge-Hub-Cloud Cyber-Physical Systems

TL;DR

This paper introduces an exact multi-objective workflow scheduling method for IoT cyber-physical systems that optimizes latency, energy, and reliability while satisfying multiple constraints, outperforming heuristics.

Contribution

It presents a novel continuous-time mixed integer linear programming approach that jointly optimizes multiple objectives and constraints in IoT workflow scheduling.

Findings

Achieves up to 29.83% reduction in latency.

Reduces energy consumption by up to 33.96%.

Improves reliability by up to 28.49%.

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.