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Reliability-Aware Fog Resource Provisioning for Deadline-Driven IoT Services

机译:截止日期驱动的IoT服务的可靠性感知雾资源供应

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Rapid growth of Internet of Things (IoT) services with different service level agreement (SLA) requirements has called for offloading computing tasks from the resource constrained IoT devices to the remote cloud. The growing number of IoT devices is exacerbating the links between IoT devices and the cloud. Furthermore, many services (e.g., health and disaster response) require fast service response and immediate data analytics, thus posing a great challenge to the remote cloud. Fog computing provides the solution by bringing computing resources to the edge of the network to assume substantial computing tasks. However, the resource failures during service processing should not be overlooked because they can greatly degrade service performance. In our work, we investigate the fog resource provisioning problem for the deadline-driven IoT services to minimize the system cost considering the probability of resource failures. We formulate the problem as an integer linear programming (ILP) model, and then design a Weighted Best Fit Decreasing (WBFD) algorithm with low computational complexity. Simulation results validate that our proposed algorithm performs close to the optimal solutions of ILP.
机译:具有不同服务水平协议(SLA)要求的物联网(IoT)服务的快速增长要求将计算任务从资源受限的IoT设备转移到远程云。越来越多的物联网设备正在加剧物联网设备与云之间的联系。此外,许多服务(例如,健康和灾难响应)需要快速的服务响应和即时的数据分析,从而对远程云提出了巨大的挑战。雾计算通过将计算资源带到网络边缘来承担大量计算任务,从而提供了解决方案。但是,不应忽略服务处理期间的资源故障,因为它们会大大降低服务性能。在我们的工作中,我们研究了截止日期驱动的物联网服务的雾资源供应问题,以考虑到资源故障的可能性来最大程度地降低系统成本。我们将问题表述为整数线性规划(ILP)模型,然后设计一种计算复杂度较低的加权最佳拟合递减(WBFD)算法。仿真结果验证了我们提出的算法性能接近于ILP的最优解。

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