Dynamic Fine-Grained SLA Management for 6G eMBB-Plus Slice Using mDNN & Smart Contracts

Abstract

The advent of 6G networks promises revolutionary advances in dynamism, intelligence, and decentralization. Realizing the full potential of 6G requires adaptable service level agreements (SLAs) that can optimize performance based on dynamic network conditions. In this paper, we suggested a method based on the Hyperledger Sawtooth blockchain’s smart contract with the Reptile meta-learning algorithm to solve the rigidity of static SLA and centralization problems. In order to sustain the quality of service in the radio access network and core network domain of 6G networks, this work focuses on SLA management for efficient resource allocation for the eMBB-plus slice. Our approach entails breaking down static SLAs into finer-grained components, transferring those components onto Hyperledger Sawtooth smart contracts, and using the Reptile meta-learning algorithm to forecast SLA metrics and resource requirements. A dynamic tariff model, also proposed within the smart contract, handles increased user demands. We evaluate the solution by analyzing Reptile performance, resource allocation, and SLA violations under dynamic demands. Results demonstrate the efficiency of this AI-driven, blockchain-based approach for automated, optimized 6G eMBB-plus resource management adhering to dynamic fine-grained SLAs. This work highlights the synergistic potential of AI and blockchain for trusted and intelligent 6G service delivery.