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Multimedia service orchestration in multi-tier edge computing environments

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BORIS DOI
10.48549/6255
Abstract
High Definition Video on Demand (VoD) and immersive multimedia services, such as Virtual Reality (VR) and Augmented Reality (AR), have been attracting thousands of new users every day for digital spaces. Static and mobile users usually watch multimedia services during entertainment, retailing, gaming, training, traveling, advertising, and commuting activities. However, multimedia services have stringent requirements in terms of Quality of Service (QoS) and Quality of Experience (QoE) support for static and/or mobile users in heterogeneous cloud-based ecosystems. Thus, new efficient approaches for orchestrating networking and computing resources and distributing multimedia flows in multi-tier mobile edge computing environments are required. The orchestration schemes must be developed to choose suitable edge nodes to execute multimedia-based services while improving the usage of networking and computing resources and providing QoE/QoS support for static and/or mobile users. This thesis contributes to efficiently meeting VoD, VR, and AR services needs in multi-tier edge computing environments. The first contribution is a service orchestrator to define an appropriate edge node to stream VoD, considering QoE, QoS, and monetary costs. The second contribution is a mobile-aware orchestrator that cooperates with edge nodes on the ground space and selects and places flying edge nodes to improve multimedia flow distribution with QoS support in congestion or failure periods. The third contribution is a mobility-aware Service Function Chaining (SFC) orchestrator for static and mobile scenarios. The proposed scheme aims to map, instantiate, and re-instantiate Service Function (SF) into multiple edge nodes considering networking and computing parameters. The proposed solutions were widely compared to related works on different scenarios. The results show that the proposed approaches outperform the state-of-the-art schemes.
Date of Publication
2023
Year of graduation
2023
Theses Type
dissertation
Subject(s)
000 Computer science, knowledge & systems
500 Science > 510 Mathematics
Language(s)
en
Author(s)
Melo dos Santos, Hugo Leonardoorcid-logo
Faculty/Graduate School
Faculty of Science
Institute
Institute of Computer Science
Federal University of Para
Access(Rights)
open.access
Primary OA Publication
true
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