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AuthorMhaisen N.
AuthorAllahham M.S.
AuthorMohamed A.
AuthorErbad A.
AuthorGuizani M.
Available date2022-04-21T08:58:22Z
Publication Date2021
Publication NameIEEE Transactions on Network Science and Engineering
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/TNSE.2021.3118970
URIhttp://hdl.handle.net/10576/30070
AbstractService provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying fixed allocation criteria in SCs does not necessarily lead to the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, making the original allocation suboptimal. Furthermore, updating the criteria manually at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, we propose a set of light-weight agents for SCs that are capable of optimizing the performance. We also propose using online learning SCs, empowered by Deep Reinforcement Learning (DRL) agent, that leverage the chained data to continuously self-tune its allocation criteria. We show that the proposed learning-assisted method achieves superior performance on the combinatorial multi-stage allocation problem while still being executable in real-time. We also compare the proposed approach with standard heuristics as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks. IEEE
Languageen
PublisherIEEE Computer Society
SubjectBlockchain
Cost benefit analysis
Costs
Deep learning
Economic and social effects
Edge computing
Heuristic methods
Internet of things
Job analysis
Optimization
Peer to peer networks
Quality of service
Reinforcement learning
Block-chain
Edge computing
Peer-to-peer computing
Performance
Resource management
Service provisioning
Smart agents
Task analysis
Vehicle's dynamics
Smart contract
TitleOn Designing Smart Agents for Service Provisioning in Blockchain-powered Systems
TypeArticle
dc.accessType Abstract Only


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