Smart cities represent an archetypal example of infrastructures where the fog computing paradigm can express its potential: we have a large set of sensors deployed over a large geographic area where data should be pre-processed (e.g., to extract relevant information or to filter and aggregate data) before sending the result to a collector that may be a cloud data center, where relevant data are further processed and stored.

However, during its lifetime the infrastructure may change, e.g., due to the additional sensors or fog nodes deploy, while the load can grow, e.g., for additional services based on the collected data. Since nodes are typically deployed in multiple time stages, they may have different computation capacity due to technology improvements. In addition, an uneven distribution of the workload intensity can arise, e.g., due to hot spot for occasional public events or to rush hours and users’ behavior. In simple words, resources and load can vary over time and space.

Under the resource management point of view, this scenario is clearly challenging. Due to the large scale and variable nature of the resources, classical centralized solutions should in fact be avoided, since they do not scale well and require to transfer all data from sensors to a central hub, distorting the very nature of in-situ data processing.

In this paper, we address the problem of resources management by proposing two distributed load balancing algorithms, tailored to deal with heterogeneity. We evaluate the performance of such algorithms using both a simplified environment where we perform several sensitivity analysis with respect to the factors responsible for the infrastructure heterogeneity and exploiting a realistic scenario of a smart city. Furthermore, in our study we combine theoretical models and simulation. Our experiments demonstrate the effectiveness of the algorithms under a wide range of heterogeneity, overall providing a remarkable improvement compared to the case of not cooperating nodes.

智慧城市代表了雾计算范例可以表达其潜力的基础设施的原型示例：我们在一大片地理区域上部署了大量传感器，在这些区域中应进行数据预处理（例如，提取相关信息或进行过滤和汇总）数据）发送给可能是云数据中心的收集器，然后在该收集器中进一步处理和存储相关数据。

但是，在其生命周期中，基础架构可能会更改，例如，由于部署了额外的传感器或雾节点，而负载可能会增加，例如，基于所收集的数据的其他服务。由于节点通常部署在多个时间阶段，因此由于技术的改进，它们可能具有不同的计算能力。另外，例如由于偶尔的公共事件的热点或高峰时间和用户的行为，可能导致工作负荷强度的不均匀分布。简而言之，资源和负载会随时间和空间而变化。

从资源管理的角度来看，这种情况显然具有挑战性。由于资源的规模大和性质多变，实际上应该避免使用传统的集中式解决方案，因为它们无法很好地扩展，并且需要将所有数据从传感器传输到中央集线器，从而扭曲了原地数据处理的本质。

在本文中，我们通过提出两种针对异构性的分布式负载平衡算法来解决资源管理问题。我们使用简化的环境评估这种算法的性能，在简化的环境中，我们对导致基础设施异质性的因素进行了几项敏感性分析，并探索了智能城市的现实情况。此外，在我们的研究中，我们将理论模型与仿真相结合。我们的实验证明了该算法在广泛的异构性下的有效性，与不协作节点的情况相比，总体上提供了显着的改进。