移动边缘计算环境中基于区块链的去中心化主动缓存策略

Jingpan Bai, Silei Zhu, Houling Ji
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摘要

在移动边缘计算(MEC)环境中,边缘缓存可以为智能场景提供及时的数据响应服务。然而,由于边缘节点的存储容量有限以及节点的恶意行为,如何选择缓存内容并实现去中心化的安全数据缓存面临挑战。本文针对这一问题,提出了一种在 MEC 环境中基于区块链的去中心化主动缓存策略。新颖之处在于,在 MEC 环境中采用了基于节点效用的区块链主动缓存策略,并构建了相应的优化问题。采用区块链是为了建立一个安全可靠的服务环境。采用的方法是基于线性松弛技术和内点法实现最优缓存策略。此外,在内容缓存系统中,缓存空间和节点效用之间存在权衡,提出了缓存策略来解决这一问题。区块链共识过程延迟与内容缓存延迟之间也存在权衡问题。为了减少共识过程延迟对内容缓存的影响,采用了离线共识认证方法。主要发现是,所提出的算法可以降低延迟,并能确保物联网环境下数据缓存的安全性。最后,仿真实验表明,与随机内容缓存算法相比,所提出的算法在缓存命中率、平均内容响应延迟和平均系统效用方面分别提高了 49.32%、43.11% 和 34.85%;与贪婪内容缓存算法相比,所提出的算法在缓存命中率、平均内容响应延迟和平均系统效用方面分别提高了 9.67%、8.11% 和 5.95%。
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Blockchain Based Decentralized and Proactive Caching Strategy in Mobile Edge Computing Environment
In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.
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