Hierarchical Service Composition via Blockchain-enabled Federated Learning
Abstract In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) an...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-08-01
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| Series: | Data Science and Engineering |
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| Online Access: | https://doi.org/10.1007/s41019-024-00258-7 |
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| author | Li Huang Lu Zhao Yansong Liu Yao Zhao |
| author_facet | Li Huang Lu Zhao Yansong Liu Yao Zhao |
| author_sort | Li Huang |
| collection | DOAJ |
| description | Abstract In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach. |
| format | Article |
| id | doaj-art-f9e29c9f9c7d40858a1ef8c953466318 |
| institution | Kabale University |
| issn | 2364-1185 2364-1541 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Data Science and Engineering |
| spelling | doaj-art-f9e29c9f9c7d40858a1ef8c9534663182024-12-08T12:39:03ZengSpringerOpenData Science and Engineering2364-11852364-15412024-08-019444946310.1007/s41019-024-00258-7Hierarchical Service Composition via Blockchain-enabled Federated LearningLi Huang0Lu Zhao1Yansong Liu2Yao Zhao3Jiangsu Open UniversityCollege of Computer, Nanjing University of Posts and TelecommunicationsDepartment of Computer Science, RWTH Aachen UniversitySchool of Information Technology, Deakin UniversityAbstract In recent years, the transformative evolution of cloud computing has reshaped organizational practices by enabling the outsourcing of web service applications. This shift has led to the emergence of the cloud environment, characterized by the involvement of Cloud Service Providers (CSPs) and intelligent applications. Cloud Service Composition (CSC) has become pivotal in this context, playing a crucial role in enhancing efficiency, Quality of Service (QoS), and customer satisfaction through the aggregation of diverse Cloud Services (CSs) to create composite services. However, the vast array of available CSs presents a challenge in efficiently addressing specified QoS requirements, turning CSC into a recognized NP-hard problem. Existing solutions, often involving third-party brokers, struggle with scalability in large-scale systems and overlook crucial security concerns. To address these limitations, we propose the Hierarchical Service Composition (HSC) approach, leveraging blockchain and federated learning to minimize computational complexity. The integration of Blockchain-enabled Federated Learning (BFL) facilitates machine learning model training with decentralized data, ensuring practicality and fairness. HSC comprises an initialization phase and two selection layers. The first selection layer enables each CSP to efficiently select services using a pre-trained model, while the second selection layer employs a blockchain-based QoS-aware mechanism for the final composition result, addressing privacy concerns. HSC introduces a novel framework, collaborative service selection methods, and a smart selection algorithm, demonstrating remarkable composition efficiency in extensive simulations compared to the baseline approach.https://doi.org/10.1007/s41019-024-00258-7Service compositionBlockchainFederated learningModel-based |
| spellingShingle | Li Huang Lu Zhao Yansong Liu Yao Zhao Hierarchical Service Composition via Blockchain-enabled Federated Learning Data Science and Engineering Service composition Blockchain Federated learning Model-based |
| title | Hierarchical Service Composition via Blockchain-enabled Federated Learning |
| title_full | Hierarchical Service Composition via Blockchain-enabled Federated Learning |
| title_fullStr | Hierarchical Service Composition via Blockchain-enabled Federated Learning |
| title_full_unstemmed | Hierarchical Service Composition via Blockchain-enabled Federated Learning |
| title_short | Hierarchical Service Composition via Blockchain-enabled Federated Learning |
| title_sort | hierarchical service composition via blockchain enabled federated learning |
| topic | Service composition Blockchain Federated learning Model-based |
| url | https://doi.org/10.1007/s41019-024-00258-7 |
| work_keys_str_mv | AT lihuang hierarchicalservicecompositionviablockchainenabledfederatedlearning AT luzhao hierarchicalservicecompositionviablockchainenabledfederatedlearning AT yansongliu hierarchicalservicecompositionviablockchainenabledfederatedlearning AT yaozhao hierarchicalservicecompositionviablockchainenabledfederatedlearning |