Joint Optimization of Task Partial Offloading and Resource Allocation in a Dual-Blockchain-Enabled MEC System with Parallelism Constraints

Integrating data security with resource management enhances security, efficiency, and reliability of blockchain-enabled mobile edge computing (MEC) systems. However, challenges such as secure data storage, timely task execution, and limited parallelism introduce complexities in task offloading decis...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on communications S. 1
Hauptverfasser: Huang, Xiaowen, Huang, Tao, Zhao, Shuguang, Xiang, Wei, Zhang, Wenqian, Zhang, Guanglin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 2025
Schlagworte:
blockchain
Blockchains
Computational modeling
Delays
Heuristic algorithms
Memory
Mobile edge computing
Optimization
Parallel processing
parallelism
Reliability
Resource management
Servers
task partial offloading
ISSN:0090-6778, 1558-0857
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Integrating data security with resource management enhances security, efficiency, and reliability of blockchain-enabled mobile edge computing (MEC) systems. However, challenges such as secure data storage, timely task execution, and limited parallelism introduce complexities in task offloading decisions and resource allocation strategies. To address these challenges, the task latency minimization problem in blockchain-enabled MEC networks is formulated as an NP-hard optimization problem. The model incorporates constraints on parallelism, partial task offloading, bandwidth and computation resource allocation among mobile users (MUs) and edge servers (ESs). To enhance the reliability and transparency of data storage, a dual-blockchain framework is proposed, consisting of multiple MU blockchains and a dedicated ES blockchain. To tackle the NP-hard problem, the original optimization problem is decomposed into multiple sub-problems, facilitating parameter decoupling. An alternating optimization algorithm is employed to refine task offloading decisions and resource allocation of MUs and ESs with limited parallelism. The ESs update their strategies iteratively based on feedback mechanisms. Additionally, a task prioritization formulation is developed to enhance scalability, considering sub-level task importance, urgency, and first-level task classification. Extensive simulation experiments demonstrate that the proposed algorithm achieves lower task latency compared to existing methods across varying network sizes, offloading schemes, and parallelism constraints. By optimizing the parallel processing of tasks, the waiting latency of this algorithm is reduced on average by 35. 35%, 57. 16% and 35. 35% compared to other methods, respectively.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2025.3610167