The conceptual approach known as Fog/Edge Computing has recently emerged, aiming to move part of the computing and storage resources from the cloud to the edge of the network. The combination of IoT devices, edge nodes, and the Cloud gives rise to a three-tier Cloud of Things (CoT) architecture. In the complex and dynamic CoT ecosystems, a key issue is how to efficiently and effectively allocate resources to meet the demands of applications. Similar to traditional clouds, the goal of resource allocation in the CoT is to maximize the number of applications served by the infrastructure while ensuring a target operational cost. We propose a resource allocation algorithm for CoT systems that (i) supports heterogeneity of devices and applications, (ii) leverages the distributed nature of edge nodes to promote collaboration during the allocation process and (iii) provides an efficient usage of the system resources while meeting latency requirements and considering different priorities of IoT applications. Our algorithm follows a heuristic-based approach inspired on an economic model for solving the resource allocation problem in CoT. A set of simulations were performed, with promising results, showing that our collaborative resource allocation algorithm is more scalable, reduces the response time for applications and the energy consumption of end devices, in comparison to a two-tier, Cloud-based approach. Moreover, the network traffic between edge nodes, and between the Edge and Cloud tiers, is considerably smaller when using our collaborative solution, in comparison to other evaluated approaches.

最近出现了一种称为“雾/边缘计算”的概念方法，旨在将部分计算和存储资源从云移动到网络边缘。物联网设备，边缘节点和云的结合产生了三层的物联网（CoT）架构。在复杂而动态的CoT生态系统中，关键问题是如何有效地分配资源以满足应用程序的需求。与传统云类似，CoT中资源分配的目标是在确保目标运营成本的同时，最大化基础架构服务的应用程序数量。我们为CoT系统提出一种资源分配算法，该算法（i）支持设备和应用程序的异构性，（ii）在分配过程中利用边缘节点的分布式特性促进协作，（iii）在满足延迟要求并考虑物联网应用程序的不同优先级的同时，有效利用系统资源。我们的算法遵循启发式方法，该方法启发了一种经济模型来解决CoT中的资源分配问题。与基于云的两层方法相比，进行了一系列仿真，结果令人满意，表明我们的协作资源分配算法具有更高的可扩展性，减少了应用程序的响应时间并减少了终端设备的能耗。此外，使用我们的协作解决方案时，边缘节点之间以及边缘和云层之间的网络流量要小得多，