Fog computing is an emerging paradigm that supplies storage, computation, and networking resources between traditional cloud data centers and end devices. This article focuses on the resource provisioning problem in collaborative fog computing for multiple delay-sensitive users. Our goal is to implement a resource provisioning strategy for network operators to minimize the total monetary cost by considering the deadline and capacity constraints. Two scenarios are considered: unlimited-processor fog nodes (UPFN) and limited-processor fog nodes (LPFN). In either scenario, we prove that the resource provisioning problem is NP-hard. First, we consider the UPFN scenario that the processors of fog nodes are unlimited and users' requests can be ideally processed in parallel. Two algorithms are proposed which greedily delete fog nodes based on the local or global collaborative influences until there is no feasible provisioning to guarantee the deadline of users. Then we extend the resource provisioning problem to a more realistic and complicated scenario LPFN in which the scheduling delay cannot be ignored. Two types of tasks are considered. One is the arbitrarily divided tasks, and a near-optimal solution bounded by has been found. m is the number of fog nodes, and is the upper bound on the Lipschitz constant of the delay function. Another one is the application-driven tasks, and we propose a heuristic algorithm. Extensive experiments validate the efficiency of the proposed algorithms.

中文翻译：

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为多个延迟敏感用户提供协作雾计算中的资源配置

雾计算是一种新兴范式，它在传统云数据中心和终端设备之间提供存储、计算和网络资源。本文重点关注多个延迟敏感用户的协作雾计算中的资源配置问题。我们的目标是通过考虑截止日期和容量限制，为网络运营商实施资源供应策略，以最大限度地减少总货币成本。考虑了两种情况：无限处理器雾节点 (UPFN) 和有限处理器雾节点 (LPFN)。在任何一种情况下，我们都证明资源供应问题是 NP-hard 问题。首先，我们考虑UPFN场景，雾节点的处理器是无限的，用户的请求可以理想地并行处理。提出了两种基于局部或全局协作影响贪婪地删除雾节点的算法，直到没有可行的规定来保证用户的截止日期。然后我们将资源供应问题扩展到更现实和更复杂的场景 LPFN，其中不能忽略调度延迟。考虑两种类型的任务。一种是任意划分的任务，以及有界的近最优解已被发现。m是雾节点的数量，是延迟函数的 Lipschitz 常数的上限。另一个是应用程序驱动的任务，我们提出了一种启发式算法。广泛的实验验证了所提出算法的效率。