D2D caching assists mobile edge caching in offloading inter-domain traffic by sharing cached items with nearby users, while its performance relies heavily on caching nodes' sharing willingness. In this article, a Blockchain-based CDM is proposed as an incentive mechanism for the distributed caching system. Under given incentive mechanisms, both D2D and mobile edge caching nodes' willingness is guaranteed by satisfying their expected reward for cache sharing. Besides, for the distributed CDM, content delivery related transactions are executed by smart contracts. To achieve consensus on the transactions and prevent frauds, a consensus protocol among the SCENE is necessary. The pPBFT protocol is proposed to minimize the latency of reaching consensus while guaranteeing its confidence level. We build the model of cache sharing and transaction execution consensus, and we further formulate cache placement and SCENE selection as Markov Decision Process problems. Considering the complexity and dynamics of the problems, a deep reinforcement learning approach is adopted to solve the problems. Simulation results show that, compared to conventional solutions, the proposed schemes achieve efficient traffic offloading, and significantly improve the transaction execution consensus speed.

中文翻译：

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区块链激励的 D2D 和移动边缘缓存：深度强化学习方法


D2D 缓存通过与附近用户共享缓存项目来协助移动边缘缓存卸载域间流量，而其性能在很大程度上依赖于缓存节点的共享意愿。在本文中，提出了一种基于区块链的 CDM 作为分布式缓存系统的激励机制。在给定的激励机制下，D2D和移动边缘缓存节点的意愿都是通过满足其对缓存共享的预期奖励来保证的。此外，对于分布式CDM，内容交付相关的交易由智能合约执行。为了达成交易共识并防止欺诈，SCENE 之间需要达成共识协议。 pPBFT协议的提出是为了在保证其置信水平的同时最大限度地减少达成共识的延迟。我们构建了缓存共享和事务执行共识的模型，并进一步将缓存放置和场景选择制定为马尔可夫决策过程问题。考虑到问题的复杂性和动态性，采用深度强化学习方法来解决问题。仿真结果表明，与传统方案相比，所提出的方案实现了高效的流量分流，并显着提高了交易执行共识速度。