[论文解读] An Online Optimization Framework for Distributed Fog Network Formation with Minimal Latency

Gilsoo Lee, Walid Saad+1 more|arXiv (Cornell University)|Oct 14, 2017

IoT and Edge/Fog Computing被引用 5

一句话总结

本文提出了一种用于边缘计算环境中动态雾网络构建的在线优化框架，采用基于阈值的算法选择邻近雾节点，以最小化端到端延迟。通过迭代更新目标竞争比并优化雾节点与云之间的任务卸载，该框架相比基线在线秘书方法将延迟降低了最多19.25%。

ABSTRACT

Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation by jointly exploiting the radio and computing resources of end-user devices and cloud servers. However, the dynamic and distributed formation of local fog networks is highly challenging due to the unpredictable arrival and departure of neighboring fog nodes. Therefore, a given fog node must properly select a set of neighboring nodes and intelligently offload its computational tasks to this set of neighboring fog nodes and the cloud in order to achieve low-latency transmission and computation. In this paper, the problem of fog network formation and task distribution is jointly investigated while considering a hybrid fog-cloud architecture. The goal is to minimize the maximum computational latency by enabling a given fog node to form a suitable fog network and optimize the task distribution, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel online optimization framework is proposed in which the neighboring nodes are selected by using a threshold-based online algorithm that uses a target competitive ratio, defined as the ratio between the latency of the online algorithm and the offline optimal latency. The proposed framework repeatedly updates its target competitive ratio and optimizes the distribution of the fog node's computational tasks in order to minimize latency. Simulation results show that the proposed framework can successfully select a set of neighboring nodes while reducing latency by up to 19.25% compared to a baseline approach based on the well-known online secretary framework. The results also show how, using the proposed framework, the computational tasks can be properly offloaded between the fog network and a remote cloud server in different network settings.

研究动机与目标

• 解决在节点到达和离开具有不可预测性的情况下，动态分布式雾网络构建的挑战。
• 通过联合优化网络构建与任务分发，最小化雾-云计算中的最大通信与计算延迟。
• 设计一种可扩展的实时解决方案，适用于节点可用性和资源状态不确定的环境。
• 在混合架构中实现本地雾节点与远程云服务器之间高效的任务卸载。
• 在在线环境下实现低延迟性能，并提供可证明的竞争比保证。

提出的方法

• 基于目标竞争比的阈值在线算法选择邻近雾节点，平衡延迟与可行性。
• 该框架动态更新竞争比，以随时间提高网络构建的成功概率。
• 延迟建模为传输延迟、计算延迟和排队延迟之和，关键变量包括数据速率、处理速度和缓冲区大小。
• 算法使用基于距离、处理速率和干扰累积分布函数的概率边界进行节点选择。
• 通过将距离、处理速率和干扰项视为独立事件，推导出网络构建概率的下界。
• 系统优化选定雾节点与云之间的任务分配，以最小化最大端到端延迟。

实验结果

研究问题

• RQ1 当邻近节点不可预测地到达时，雾节点如何动态构建低延迟网络？
• RQ2 何种在线算法可在不确定性条件下实现雾网络构建的可证明有界竞争比？
• RQ3 与现有基于在线秘书的方法相比，所提出的框架在延迟方面有何改进？
• RQ4 自适应竞争比更新对网络构建成功率和延迟降低有何影响？
• RQ5 在混合架构中，如何在雾节点与云之间最优地卸载计算任务？

主要发现

• 与基线在线秘书方法相比，所提出的框架将端到端延迟降低了最多19.25%。
• 该框架成功构建功能性雾网络，其概率下限由涉及节点距离、处理速率和干扰的推导表达式所界定。
• 自适应竞争比更新显著提高了网络构建的成功率和延迟最小化效果。
• 系统通过智能地在本地雾节点与远程云之间分配工作负载，实现了低延迟任务卸载。
• 理论分析表明，随着观察试验次数增加和竞争比边界收紧，成功构建网络的概率也随之提高。
• 仿真结果验证了该框架在多样化网络设置和动态节点到达模式下的有效性。