Design and Management of Fog Networks

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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.**