Browsing by Author "Manner, Jukka, Prof., Aalto University, Department of Information and Communications Engineering, Finland"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Improving Live Video Streaming Performance for Smart City Services
    (Aalto University, 2024) El Marai, Oussama; Informaatio- ja tietoliikennetekniikan laitos; Department of Information and Communications Engineering; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Manner, Jukka, Prof., Aalto University, Department of Information and Communications Engineering, Finland
    Our world is rapidly moving in all its aspects toward a more digitized and connected life, including transportation, education, farming, and healthcare. A major enabler for such transformation is ICT-related tremendous innovations in networking, computation, and storage, both in software and hardware at affordable prices. Owing to these phenomenal advances, many revolutionary paradigms, such as multi-access edge computing, self-driving vehicles, and Smart Cities, have emerged, promising rosy prospects and a flourishing future. An eminent feature of these futuristic technologies is automation, where objects can communicate (i.e., sending and receiving data), understand their environment, and adapt to changing conditions by taking the right decisions. Also, stringent requirements (e.g., low latency communication) might be needed by many services for their proper functioning. To successfully accomplish these tasks, many paradigms (e.g., software-defined networking and machine learning techniques) should be involved at different levels (e.g., network and decision-making levels). Most of today's applications and systems (e.g., over-the-top and surveillance platforms) require video streaming as a key technology. Video streaming applications rank as the most bandwidth-intensive services, especially when delivered at higher resolutions, such as FHD and 4K. Fortunately, 5G technology is already available and promises higher bandwidth that can reach up to 20GB. In addition, it requires huge data storage spaces when historical data is needed, which no longer becomes an issue with the dawn of edge and cloud computing. The target consumer (i.e., humans or machines) might demand heavy computation resources, often requiring GPU processing, which is also nowadays readily available and affordable. This dissertation is all about harnessing video streaming technology for enabling Smart City services and paradigms, such as self-driving vehicles. Towards this end, we start by addressing the problem of improving video streaming performance in terms of delivered video quality, stall-free sessions, and low latency streaming, for various services, including video streaming services and some use cases of self-driving vehicles. As data is the fuel that empowers most Smart City systems and services, we propose a cost-efficient and sustainable solution to create the digital twin of city roads, which mainly relies on video streaming data. The proposed solution represents an essential step towards realizing the Smart City paradigm and would create a valuable data asset that feeds and benefits various systems and domains such as intelligent transportation systems and tourism. Owing to the extreme importance of situational awareness in Smart Cities, notably in dense urban areas, we leverage the proposed digital twinning solution and machine learning techniques to raise the awareness of connected vehicles about their surroundings, as well as overall street awareness per defined regions while accounting for the amount of transmitted data over the network to avoid video streaming performance degradation.
  • Thumbnail Image
    Item
    Network Slice Mobility and Service Function Chain Migration across Multiple Administrative Cloud Domains
    (Aalto University, 2024) Addad, Rami Akrem; Dutra, Diego Leonel Cadette, Prof., Federal University of Rio de Janeiro, Brazil; Tietoliikenne- ja tietoverkkotekniikan laitos; Department of Communications and Networking; Sähkötekniikan korkeakoulu; School of Electrical Engineering; Manner, Jukka, Prof., Aalto University, Department of Information and Communications Engineering, Finland
    The maturing 5G network technology sees growing commercial deployments, with a shifting focus to service delivery. 5G networks, a common platform for diverse services, utilize network slicing for service isolation. Cloud-native services, composed of interdependent micro-services, are allocated to network slices spanning multiple areas, domains, and data centers. Due to mobility events caused by mobile end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This requires slice mobility, which involves a slice moving between service areas. Slice mobility requires the inter-dependent service and resources to be migrated to reduce system overhead and to ensure low-communication latency by following end-user mobility patterns. Recent advances in computational hardware, Artificial Intelligence, and Machine Learning have attracted interest within the communication community, with increased research interest in self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process given the needed coordination between inter-cloud resources, the dynamics, and the constraints of inter-data center networks. In this regard, this dissertation defines and enables smooth network slicing mobility patterns while maintaining both system and network resources stable. Specifically, we design, implement, and evaluate our proposed migration framework. Then, we design and define different network slice mobility patterns with their corresponding grouping methods and relevant mobility triggers. Next, we introduce various SFC migration strategies as an underlay technology enabler for network slice mobility patterns. After that, we propose an agent for automating the triggers selection process for enabling various network slice mobility patterns. Finally, we develop a network-aware agent capable of selecting accurate bandwidth values while ensuring fast and reliable service migration, thus enabling slice mobility while matching network and system requirements. In each section of this dissertation, the research results are evaluated and validated under different configurations in real-world settings or simulated environments. This dissertation provides recommendations for improving and extending the notion of mobility in network slices while also highlighting the various outstanding questions and suggesting future challenges and research directions.