TY - GEN
T1 - Rational Contracts
T2 - 2021 IEEE International Conference on Communications, ICC 2021
AU - Mhaisen, Naram
AU - Mohamed, Amr
AU - Erbad, Aiman
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - Smart Contracts (SCs), which are software programs that run on blockchain platforms, provide appealing security guarantees characterized by decentralized, autonomous, and verifiable execution. On the other hand, Service Provisioning (SP) systems (i.e., systems that assign users to service providers in a way that maximizes the global utility) have been leveraging SCs to provide trust and transparency features. Such features are obtained by deploying the SP's assignment criteria as an SC on the blockchain. However, deploying optimal assignment criteria as SCs does not guarantee the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, potentially deeming the initial assignment sub-optimal. Furthermore, modifying the criteria manually by a third party at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, in this paper, we propose the use of online learning SCs that leverage the chained data to continuously self-tune their assignment criteria and maintain maximum utility. We show that the proposed data-driven method can achieve high performance on the multi-stage assignment problem. We also compare the proposed approach to multiple assignment algorithms as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks compared to planning techniques.
AB - Smart Contracts (SCs), which are software programs that run on blockchain platforms, provide appealing security guarantees characterized by decentralized, autonomous, and verifiable execution. On the other hand, Service Provisioning (SP) systems (i.e., systems that assign users to service providers in a way that maximizes the global utility) have been leveraging SCs to provide trust and transparency features. Such features are obtained by deploying the SP's assignment criteria as an SC on the blockchain. However, deploying optimal assignment criteria as SCs does not guarantee the best performance over time since the blockchain participants join and leave flexibly, and their load varies with time, potentially deeming the initial assignment sub-optimal. Furthermore, modifying the criteria manually by a third party at every variation in the blockchain jeopardizes the autonomous and independent execution promised by SCs. Thus, in this paper, we propose the use of online learning SCs that leverage the chained data to continuously self-tune their assignment criteria and maintain maximum utility. We show that the proposed data-driven method can achieve high performance on the multi-stage assignment problem. We also compare the proposed approach to multiple assignment algorithms as well as planning methods. Results show a significant performance advantage over heuristics and better adaptability to the dynamic nature of blockchain networks compared to planning techniques.
KW - Blockchain
KW - Distributed ledger
KW - Reinforcement learning
KW - Service provisioning
KW - Smart contracts
UR - https://www.scopus.com/pages/publications/85115680923
U2 - 10.1109/ICC42927.2021.9500372
DO - 10.1109/ICC42927.2021.9500372
M3 - Conference contribution
AN - SCOPUS:85115680923
T3 - IEEE International Conference on Communications
BT - ICC 2021 - IEEE International Conference on Communications, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 June 2021 through 23 June 2021
ER -