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• W4320002804 abstract "We investigate the computing resource allocation in multi-access edge network slicing (NS) in the context of revenue and multi-access edge computing (MEC) resource management. The significant variety of slice resource utilization levels across slice tenants (i. e., Mobile Virtual Network Operators (MVNOs)) challenges MEC resource management in NS with MEC, leading to virtual machine resource (VMR) (i. e., computing resource) wastage or scarcity. As a result, for efficient MEC resource management, the infrastructure provider (InP) encourages dynamic resource sharing and trading (DRST) of unutilized slice VMR quotas. Nevertheless, cellular network security and privacy issues deter MVNOs from collaborating on effective DRST. The security characteristics inherent in blockchain have recently gained much interest for secure resource trading. Thus, this paper proposes a unique hierarchical blockchain-based inter-slice computing resource trading (ISCRT) scheme for peer-to-peer (P2P) MVNOs in an autonomous multi-sliced MEC-based 5G network. For secure ISCRT transactions, a consortium blockchain network with hyperledger smart contracts (SC) is designed. We model the demand and pricing problems of buyer and seller MVNOs for the unutilized VMRs using a two-stage Stackelberg game. Then, to obtain the Stackelberg equilibrium (SE), an enhanced dueling double deep Q-network (D3QN) algorithm is proposed, which intelligently determines the optimal demand and pricing policies of MVNOs for the unutilized VMRs during ISCRT transactions at negotiation intervals. Simulation analysis shows that the proposed enhanced D3QN algorithm outperforms benchmark schemes in terms of the MVNO slice-level satisfaction and VMR utilization while reducing double-spending attacks in ISCRT settings by 16% and increasing both players’ utility." @default.
• W4320002804 created "2023-02-11" @default.
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• W4320002804 date "2023-09-01" @default.
• W4320002804 modified "2023-10-15" @default.
• W4320002804 title "Blockchain-Based Computing Resource Trading in Autonomous Multi-Access Edge Network Slicing: A Dueling Double Deep Q-Learning Approach" @default.
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• W4320002804 doi "https://doi.org/10.1109/tnsm.2023.3240301" @default.
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