超密集网络中的社会信任协作边缘计算

Lixing Chen, Jie Xu
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引用次数: 63

摘要

具有类似云计算能力的小型蜂窝基站(SBSs)被认为是边缘计算(EC)的关键推动者,它为各种新兴移动应用和物联网提供超低延迟和位置感知。然而,由于单个SBS的计算资源有限,当它被过多的计算工作负载过载时,向用户提供高质量的计算服务将面临重大挑战。在本文中,我们提出了SBS之间的协同边缘计算,通过形成SBS联盟相互共享计算资源,从而在边缘系统中容纳更多的计算工作量,减少对远程云的依赖。基于联盟博弈论开发了一种新的SBS联盟形成算法,以应对基于小蜂窝的边缘系统中的各种新挑战,包括无线电接入和计算服务的共同提供、合作激励和潜在的安全风险。为了应对这些挑战,本文提出的方法(1)通过利用SBS的超密集部署,允许在用户-SBS关联阶段和SBS对等卸载阶段进行协作;(2)开发基于支付的激励机制,实现按比例公平的效用分配,形成稳定的SBS联盟;(3)建立社会信任网络,以管理由于协作而导致的SBS之间的安全风险。在实际场景中进行了系统仿真,评估了该方法的有效性和性能,结果表明该方法可以显著提高边缘计算性能。
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Socially trusted collaborative edge computing in ultra dense networks
Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.
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