Application Deployment Framework for large-scale Fog Computing Environment

Student thesis: Doctoral ThesisDoctor of Philosophy


The extension of the Cloud to the Edge of the network through Fog Computing can have a significant impact on the reliability and the latency of deployed applications. Recent papers have suggested a shift from Virtual Machines and Container based deployments to a shared environment among applications to better utilise resources. The existing deployment and optimisation methods do not account for application interdependence or the locality of application resources, which can cause inaccurate estimations. When considering models that account for these, however the optimisation task of allocating applications to gateways becomes a difficult problem to solve that requires either model simplifications or tailor-made optimisation methods. The main contribution of this research is the set of weighted deployments methods that aimtoreducethecomplexityofthesearch-spaceinlarge-scalefogdeploymentenvironments while retaining significant system characteristics. This work was attained by first addressing some existing IoT issues by proposing a Fog of Things gateway platform to answer the connectivity and protocol translation requirements. The proposed platform was used to identify the characteristics and challenges of these systems. A new data-driven reference model was then proposed to estimate the effects of application deployment and migration on these systems. Based on this model, weighted clustering and resource allocation methods are defined, that are then improved upon by a set of weight tuning methods focusing on analysing favourable and sample deployments. These proposals were validated by running tests on Industry 4.0 case studies. These varying scenarios made it possible to identify the scaling and deployment characteristics of these systems. Based on these initial tests, the second batch of physical and virtual experiments was carried out to validate the models and to evaluate the proposed methods. The findings show that the proposed application and gateway model can predict the load and delay of components to an accuracy of 91%. Within the presented scenarios, constraints and Fog sizes larger than 300 applications, the proposed weighted clustering methods were shown to significantly improve the utility of deployments. In some cases, these methods were the sole providers of valid solutions
Date of AwardSep 2020
Original languageEnglish

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