Hybrid Programming-Based Scheduling Approach for Many Heterogeneous Computing Tasks With Asynchronous Generation in IIoT

Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT e...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE internet of things journal Jg. 12; H. 15; S. 29384 - 29396
Hauptverfasser: Hu, Bingtao, Zhong, Ruirui, Wang, Yong, Wang, Tianyue, Feng, Yixiong, Zhou, MengChu, Tan, Jianrong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.08.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Cloud computing
Completion time
Computation
Computational modeling
Computing task
Constraints
Data exchange
Dynamic scheduling
Feasibility
Heuristic algorithms
Industrial applications
Industrial Internet of Things
Industrial Internet of Things (IIoT)
Job shop scheduling
Mathematical models
Mathematical programming
Optimal scheduling
optimization
Processor scheduling
Scheduling
ISSN:2327-4662, 2327-4662
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Industrial Internet of Things (IIoT) plays a crucial role in advancing smart manufacturing by connecting numerous devices, enabling data exchanges, and supporting industrial applications. Yet, the timely and proper scheduling of asynchronously generated heterogeneous computing tasks (HCTs) in IIoT environments remains a significant challenge. In this article, we first introduce the representation and notation of such HCTs and define a computing network structure. We then propose an initial mathematical programming-based scheduling model aimed at minimizing HCT completion time. To make this model easy to solve, we reformulate it by using logical constraints and derive a constraint programming-based model, for which a feasibility-guaranteed solution algorithm is developed. This algorithm leverages two easily verified propositions to either identify feasible solutions or demonstrate the infeasibility of the problem.Furthermore, we have proven a critical proposition that facilitates the development of a hybrid programming-based scheduling approach, effectively combining the strengths of both mathematical and constraint programming models. As demonstrated through extensive computational experiments, our proposed approach achieves an average reduction of 20% in HCT completion time in comparison with its existing peers. It consistently and timely provides the high-quality solutions that meet the required deadlines.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2025.3567149