Design and Management of Fog Networks

随着物联网（IoT）的出现，任何设备，无论多么小，都可以连接到互联网并监视/控制物理元素，从而使智能城市、智能医疗保健和智能交通等多种应用成为可能。通常，这些设备产生的数据通过网络基础设施传输到云，进行处理/存储。然而，随着物联网设备异常数据的速度、分布和规模，在云中处理所有数据将是不切实际的。特别是当涉及具有严格延迟要求的物联网应用时，云计算无法充分满足这些要求。为了克服这些缺陷，一种叫做雾计算的新模式出现了。这种模式已经作为一种有前途的解决方案被引入，通过将计算（和存储）能力移动到用户附近，从而在物联网-雾-云连续体的任何地方处理数据，从而支持广泛的物联网应用；目标是满足物联网应用的强制要求（例如，延迟），如果有的话，并减少网络流量的爆炸。***最近在雾基础设施的资源管理方面有了显著的贡献。事实上，已经提出了几种模型和方案来管理资源，目的是满足物联网应用的要求（例如延迟），同时最大限度地降低总体成本（例如带宽和能源）。然而，所有这些贡献都假定雾基础设施的存在。在这个项目中，我们将开发新的模型和工具，以有效地设计/规划物联网-雾-云连续体中的雾基础设施。更具体地说，我们将开发一个数学上严格的公式，以研究和分析雾设计的动力学和特性。特别是，我们计划开发混合方法，将极其高效的元启发式与数学规划方法结合起来，利用问题的结构，同时减少组合爆炸。然后，我们将开展处理关键设计问题的研究：随着时间的推移，与负载增加和地理扩展相关的设计可扩展性；构型对于故障的鲁棒性。然而，即使是最优设计，过载和不平衡利用也会发生（例如，当实际负载模式发生变化和/或超过设计阶段使用的估计/预期负载时）。因此，我们还将开发新的自适应资源管理方案，以满足动态/不确定环境中的应用需求。更具体地说，我们将制定有效的方案，允许在雾基础设施中调整/重新优化资源配置，当前支持的应用程序，以响应这些不可预测的情况；目标是为这些应用程序提供服务质量保证

With the emergence of Internet of Things (IoT), where any device, however small, is able to connect to the Internet and monitor/control physical elements, several applications were made possible such as smart cities, smart healthcare and smart transportation. Generally, data generated by these devices is transmitted, via the network infrastructure, to the cloud, to be processed/stored. However, with the rate, distribution and scale of extraordinary data from IoT devices, processing all the data in the cloud would be impractical. Particularly, when it comes to IoT applications with stringent delay requirements, cloud computing cannot adequately address these requirements. To overcome these deficiencies, a new paradigm, called fog computing, has emerged. This paradigm has been introduced as a promising solution to support wide range of IoT applications by moving the computing (and storage) power to the proximity of users, and thus processing data anywhere along the IoT-Fog-Cloud continuum; the objective is to meet the imposed requirements (e.g., delay), if any, of IoT applications and reduce the explosion in network traffic. ***Recently, there has been notable contributions in resource management of the fog infrastructure. Indeed, several models and schemes have been proposed to manage resources, with the objective to satisfy the requirements of IoT applications (e.g. delay) while minimizing the overall cost (e.g., bandwidth and energy). However, all these contributions assume the existence of the fog infrastructure. In this project, we will develop new models and tools for the efficient design/planning of fog infrastructure in the IoT-fog-cloud continuum. More specifically, we will develop a mathematically rigorous formulation, to study and analyze the dynamics and properties of fog design. In particular, we plan to develop hybrid approaches that combine extremely efficient meta-heuristics, with mathematical programming approaches that exploit the structure of the problem while curtailing combinatorial explosion. Then, we will carry out studies dealing with key design issues: the scalability of designs with respect to load increases and geographical expansion over time; and the robustness of configurations with respect to failures. However, even with an optimal design, overload and unbalanced utilization can and will happen (e.g., when actual load patterns change and/or exceed the estimated/expected load used during the design phase). Thus, we will also develop novel adaptive resource management schemes to satisfy application requirements in a dynamic/uncertain environment. More specifically, we will develop efficient schemes that allows to adapt/re-optimize resource provisioning in the fog infrastructure, of currently supported applications, in response to these unpredictable situations; the objective is to provide quality of service assurance to these applications.**