Contribution to network management of beyond 5G networks: management and orchestration architecture to support microservice-based services
(English) The thesis has contributed to the research on network management for the provisioning of future services, which we refer to as the Future Service Deployment Problem (FSDP). In this thesis, different deployment techniques facing the FSDP are investigated to achieve solving the problem in a...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/695189 |
| Acceso en línea: | http://hdl.handle.net/10803/695189 https://dx.doi.org/10.5821/dissertation-2117-440839 |
| Access Level: | acceso abierto |
| Palabra clave: | Àrees temàtiques de la UPC::Enginyeria de la telecomunicació 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions |
| Sumario: | (English) The thesis has contributed to the research on network management for the provisioning of future services, which we refer to as the Future Service Deployment Problem (FSDP). In this thesis, different deployment techniques facing the FSDP are investigated to achieve solving the problem in a cost-effective and resource-efficient manner under different constraints while meeting the requirements of selected representative future applications, as a problem to be solved in a time frame appropriate for the particular working scenario considered. Depending on the operational behaviour and network and resource requirements, four representative applications have been identified and classified into two different categories: i) Forwarding Applications (FAs), such as some smart city applications, and ii) Closed Loop Applications (CLAs), such as Virtual Reality (VR) or Vehicle Collision Avoidance (VCA). The FSDP can generally be viewed as consisting of two interlinked sub-problems that should always be addressed in a coordinated manner in order to achieve adequate deployments. In particular, the two sub-problems are: i) Where to deploy a service; and ii) How to deploy the service. For the former, we mainly consider a given set of coordinated and cooperating resources that form the substrate network. The solution to the problem is to determine an optimal compute node or subset of compute nodes and the associated links between those nodes to allocate the various microservices of a service request. While the latter deals with the appropriate provisioning of microservices that takes into account the various key attributes arising from the network state and QoS requirements of the service requests, in a resource-efficient and cost-effective manner, to be executed in a reasonable time while meeting the associated constraints. In the following, a brief mentioning of the different chapters of the thesis is given according to the proposed technique and the considered working scenario for each of them: chapters 1 and 2 present the thesis objectives, the working scenarios, the state-of-the-art regarding the addressed problem, the description of considered application use cases, a mathematical formulation of the problem, and a summary of proposed techniques to solve it. In Chapter 3, an Optimization Deployment Algorithm (ODA) is presented to obtain near-optimal deployment solutions in a reasonable amount of time, a Multiaccess Edge Computing (MEC) working scenario is assumed, where different MEC State Features (MSFs) are used. Chapter 4 introduces a deployment technique that uses a combination of heuristic and artificial intelligence (AI) to flexibly and optimally fulfil a wider range of requirements and to accommodate the various service requests on a shared network. Chapter 5 explores the use of a more complex working scenario: a hybrid edge-cloud system, where resources at the edge tier are geographically distributed. Moreover, an AI technique based on reinforcement learning (RL) is proposed. In addition, a new heuristic is proposed for the allocation of microservices within the edge network. Chapter 6 presents a metaheuristic algorithm, namely a customized Genetic Algorithm, tailored for the deployment of future microservice-based applications in a multi-tier network. Chapter 7 concludes the thesis with a summary of the main results, future work, and drawn conclusions. |
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