Browsing by Author "Addad, Rami Akrem"

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    AI-based Network-aware Service Function Chain Migration in 5G and Beyond Networks
    (IEEE, 2021-04) Addad, Rami Akrem; Dutra, Diego Leonel Cadette; Taleb, Tarik; Flinck, Hannu; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Nokia Bell Labs Finland; Federal University of Rio de Janeiro
    While the 5G network technology is maturing and the number of commercial deployments is growing, the focus of the networking community is shifting to services and service delivery. 5G networks are designed to be a common platform for very distinct services with different characteristics. Network Slicing has been developed to offer service isolation between the different network offerings. Cloud-native services that are composed of a set of inter-dependent micro-services are assigned into their respective slices that usually span multiple service areas, network domains, and multiple data centers. Due to mobility events caused by moving end-users, slices with their assigned resources and services need to be re-scoped and re-provisioned. This leads to slice mobility whereby a slice moves between service areas and whereby the inter-dependent service and resources must 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 to study and experiment self-managed network slices. However, migrating a service instance of a slice remains an open and challenging process, given the needed co-ordination between inter-cloud resources, the dynamics, and constraints of inter-data center networks. For this purpose, we introduce a Deep Reinforcement Learning based agent that is using two different algorithms to optimize bandwidth allocations as well as to adjust the network usage to minimize slice migration overhead. We show that this approach results in significantly improved Quality of Experience. To validate our approach, we evaluate the agent under different configurations and in real-world settings and present the results.
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    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.
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    Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-07) Addad, Rami Akrem; Dutra, Diego Leonel Cadette; Taleb, Tarik; Flinck, Hannu; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Nokia Bell Labs Finland; Federal University of Rio de Janeiro
    Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that delivers customizable services to new and under-serviced industry sectors. However, user mobility's impact on the optimal resource allocation within and between slices deserves more attention. Slices and their dedicated resources should be offered where the services are to be consumed to minimize network latency and associated overheads and costs. Different mobility patterns lead to different resource re-allocation triggers, leading eventually to slice mobility when enough resources are to be migrated. The selection of the proper triggers for resource re-allocation and related slice mobility patterns is challenging due to triggers' multiplicity and overlapping nature. In this paper, we investigate the applicability of two Deep Reinforcement Learning based algorithms for allowing a fine-grained selection of mobility triggers that may instantiate slice and resource mobility actions. While the first proposed algorithm relies on a value-based learning method, the second one exploits a hybrid approach to optimize the action selection process. We present an enhanced ETSI Network Function Virtualization edge computing architecture that incorporates the studied mechanisms to implement service and slice migration. We evaluate the proposed methods' efficiency in a simulated environment and compare their performance in terms of training stability, learning time, and scalability. Finally, we identify and quantify the applicability aspects of the respective approaches.