论文标题
用于无计算驱动的物联网网络的基于匹配的服务卸载
Matching-based Service Offloading for Compute-less Driven IoT Networks
论文作者
论文摘要
随着物联网(IoT)和5G网络的出现,Edge Computing为业务模型和用例创新提供了新的机会。服务提供商现在可以将云虚拟化数据中心以外的云,以满足延迟,数据主权,可靠性和互操作性要求。然而,许多新应用(例如增强现实,虚拟现实,人工智能)都是计算密集型和延迟敏感性。这些应用程序通过可能导致相同输出的相似输入进行大量调用。计算无限网络旨在实现具有最少计算和通信的网络。可以通过将普遍的服务卸载到边缘,从而最大程度地减少核心网络中的通信并使用计算重用概念消除冗余计算来实现这一点。在本文中,我们介绍了基于匹配的服务卸载计划,用于无计算的物联网网络。我们采用匹配理论将服务卸载匹配到适当的边缘服务器。具体来说,我们设计,哨声,一种垂直的多到卸载方案,旨在卸载适合适当的边缘服务器的最被调用和高度可重复使用的服务。我们进一步扩展了哨声,以在边缘服务器之间提供水平的一对多计算重复使用共享,从而导致弹跳较小的计算回到云中。我们使用现实世界中的数据集评估了哨声的效率和有效性。获得的发现表明,Whistle能够将任务的完成时间加速20%,将计算最多减少到77%,并将通信降低到71%。理论分析也证明了设计方案的稳定性。
With the advent of the Internet of Things (IoT) and 5G networks, edge computing is offering new opportunities for business model and use cases innovations. Service providers can now virtualize the cloud beyond the data center to meet the latency, data sovereignty, reliability, and interoperability requirements. Yet, many new applications (e.g., augmented reality, virtual reality, artificial intelligence) are computation-intensive and delay-sensitivity. These applications are invoked heavily with similar inputs that could lead to the same output. Compute-less networks aim to implement a network with a minimum amount of computation and communication. This can be realized by offloading prevalent services to the edge and thus minimizing communication in the core network and eliminating redundant computations using the computation reuse concept. In this paper, we present matching-based services offloading schemes for compute-less IoT networks. We adopt the matching theory to match service offloading to the appropriate edge server(s). Specifically, we design, WHISTLE, a vertical many-to-many offloading scheme that aims to offload the most invoked and highly reusable services to the appropriate edge servers. We further extend WHISTLE to provide horizontal one-to-many computation reuse sharing among edge servers which leads to bouncing less computation back to the cloud. We evaluate the efficiency and effectiveness of WHISTLE with a real-world dataset. The obtained findings show that WHISTLE is able to accelerate the tasks completion time by 20%, reduce the computation up to 77%, and decrease the communication up to 71%. Theoretical analyses also prove the stability of the designed schemes.