Enabling Artificial Intelligence Analytics on The Edge

PhD Thesis


Tsakanikas, V. (2022). Enabling Artificial Intelligence Analytics on The Edge . PhD Thesis https://doi.org/10.18744/lsbu.92w1z
AuthorsTsakanikas, V.
TypePhD Thesis
Abstract

This thesis introduces a novel distributed model for handling in real-time, edge-based video analytics. The novelty of the model relies on decoupling and distributing the services into several decomposed functions, creating virtual function chains (V F C
model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the V F C model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the V F C model have shown that it can reduce the total edge cost, compared with a monolithic and a simple frame distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Apache Spark ©), have been deployed. A cloud service has also been considered. Experiments have shown that V F C can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a migration model, a caching model and a QoS monitoring service based on Long-Term-Short-Term models are introduced.

Year2022
PublisherLondon South Bank University
Digital Object Identifier (DOI)https://doi.org/10.18744/lsbu.92w1z
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Publication dates
Print01 Nov 2022
Publication process dates
Deposited07 Dec 2022
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Related outputs

An Intelligent model for supporting Edge Migration for Virtual Function Chains in Next Generation Internet of Things
Tsakanikas, V., Dagiuklas, A., Iqbal, M., Wang, X. and Mumtaz, S. (2023). An Intelligent model for supporting Edge Migration for Virtual Function Chains in Next Generation Internet of Things. Scientific Reports. https://doi.org/10.1038/s41598-023-27674-5