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...
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| Vydáno v: | IEEE transactions on communications s. 1 |
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2025
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zhao, Shuguang Xiang, Wei Huang, Xiaowen Zhang, Wenqian Huang, Tao Zhang, Guanglin |
| Author_xml | – sequence: 1 givenname: Xiaowen orcidid: 0000-0001-6323-7070 surname: Huang fullname: Huang, Xiaowen email: huangxiaowen@mail.dhu.edu.cn organization: College of Information Science and Technology, Donghua University, Shanghai, China – sequence: 2 givenname: Tao orcidid: 0000-0002-8098-8906 surname: Huang fullname: Huang, Tao email: tao.huang1@jcu.edu.au organization: College of Science and Engineering, James Cook University, Smithfield, QLD, Australia – sequence: 3 givenname: Shuguang orcidid: 0000-0002-6359-4091 surname: Zhao fullname: Zhao, Shuguang email: sgzhao@dhu.edu.cn organization: College of Information Science and Technology, Donghua University, Shanghai, China – sequence: 4 givenname: Wei orcidid: 0000-0002-0608-065X surname: Xiang fullname: Xiang, Wei email: w.xiang@latrobe.edu.au organization: School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, VIC, Australia – sequence: 5 givenname: Wenqian orcidid: 0000-0003-2007-6478 surname: Zhang fullname: Zhang, Wenqian email: wqzhang@dhu.edu.cn organization: College of Information Science and Technology, Donghua University, Shanghai, China – sequence: 6 givenname: Guanglin orcidid: 0000-0003-4095-6843 surname: Zhang fullname: Zhang, Guanglin email: glzhang@dhu.edu.cn organization: College of Information Science and Technology, Donghua University, Shanghai, China |
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| SubjectTerms | blockchain Blockchains Computational modeling Delays Heuristic algorithms Memory Mobile edge computing Optimization Parallel processing parallelism Reliability Resource management Servers task partial offloading |
| Title | Joint Optimization of Task Partial Offloading and Resource Allocation in a Dual-Blockchain-Enabled MEC System with Parallelism Constraints |
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