Service placement and request routing in MEC networks with storage, computation, and communication constraints

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network edge, in proximity to the end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be pre-stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in dense MEC networks with multidimensional constraints. We show that this problem generalizes several well-known placement and routing problems and propose an algorithm that achieves close-to-optimal performance using a randomized rounding technique. Evaluation results demonstrate that our approach can effectively utilize available storage, computation, and communication resources to maximize the number of requests served by low-latency edge cloud servers

Joint Service Placement and Request Routing in Multi-cell Mobile Edge Computing Networks

The proliferation of innovative mobile services such as augmented reality, networked gaming, and autonomous driving has spurred a growing need for low-latency access to computing resources that cannot be met solely by existing centralized cloud systems. Mobile Edge Computing (MEC) is expected to be an effective solution to meet the demand for low-latency services by enabling the execution of computing tasks at the network-periphery, in proximity to end-users. While a number of recent studies have addressed the problem of determining the execution of service tasks and the routing of user requests to corresponding edge servers, the focus has primarily been on the efficient utilization of computing resources, neglecting the fact that non-trivial amounts of data need to be stored to enable service execution, and that many emerging services exhibit asymmetric bandwidth requirements. To fill this gap, we study the joint optimization of service placement and request routing in MEC-enabled multi-cell networks with multidimensional (storage-computation-communication) constraints. We show that this problem generalizes several problems in literature and propose an algorithm that achieves close-to-optimal performance using randomized rounding. Evaluation results demonstrate that our approach can effectively utilize the available resources to maximize the number of requests served by low-latency edge cloud servers.Comment: IEEE Infocom 201

Allocation des ressources dans les environnements informatiques en périphérie des réseaux mobiles

Abstract: The evolution of information technology is increasing the diversity of connected devices and leading to the expansion of new application areas. These applications require ultra-low latency, which cannot be achieved by legacy cloud infrastructures given their distance from users. By placing resources closer to users, the recently developed edge computing paradigm aims to meet the needs of these applications. Edge computing is inspired by cloud computing and extends it to the edge of the network, in proximity to where the data is generated. This paradigm leverages the proximity between the processing infrastructure and the users to ensure ultra-low latency and high data throughput. The aim of this thesis is to improve resource allocation at the network edge to provide an improved quality of service and experience for low-latency applications. For better resource allocation, it is necessary to have reliable knowledge about the resources available at any moment. The first contribution of this thesis is to propose a resource representation to allow the supervisory xentity to acquire information about the resources available to each device. This information is then used by the resource allocation scheme to allocate resources appropriately for the different services. The resource allocation scheme is based on Lyapunov optimization, and it is executed only when resource allocation is required, which reduces the latency and resource consumption on each edge device. The second contribution of this thesis focuses on resource allocation for edge services. The services are created by chaining a set of virtual network functions. Resource allocation for services consists of finding an adequate placement for, routing, and scheduling these virtual network functions. We propose a solution based on game theory and machine learning to find a suitable location and routing for as well as an appropriate scheduling of these functions at the network edge. Finding the location and routing of network functions is formulated as a mean field game solved by iterative Ishikawa-Mann learning. In addition, the scheduling of the network functions on the different edge nodes is formulated as a matching set, which is solved using an improved version of the deferred acceleration algorithm we propose. The third contribution of this thesis is the resource allocation for vehicular services at the edge of the network. In this contribution, the services are migrated and moved to the different infrastructures at the edge to ensure service continuity. Vehicular services are particularly delay sensitive and related mainly to road safety and security. Therefore, the migration of vehicular services is a complex operation. We propose an approach based on deep reinforcement learning to proactively migrate the different services while ensuring their continuity under high mobility constraints.L'évolution des technologies de l'information entraîne la prolifération des dispositifs connectés qui mène à l'exploration de nouveaux champs d'application. Ces applications demandent une latence ultra-faible, qui ne peut être atteinte par les infrastructures en nuage traditionnelles étant donné la distance qui les sépare des utilisateurs. En rapprochant les ressources aux utilisateurs, le paradigme de l'informatique en périphérie, récemment apparu, vise à répondre aux besoins de ces applications. L’informatique en périphérie s'inspire de l’informatique en nuage, en l'étendant à la périphérie du réseau, à proximité de l'endroit où les données sont générées. Ce paradigme tire parti de la proximité entre l'infrastructure de traitement et les utilisateurs pour garantir une latence ultra-faible et un débit élevé des données. L'objectif de cette thèse est l'amélioration de l'allocation des ressources à la périphérie du réseau pour offrir une meilleure qualité de service et expérience pour les applications à faible latence. Pour une meilleure allocation des ressources, il est nécessaire d'avoir une bonne connaissance sur les ressources disponibles à tout moment. La première contribution de cette thèse consiste en la proposition d'une représentation des ressources pour permettre à l'entité de supervision d'acquérir des informations sur les ressources disponibles à chaque dispositif. Ces informations sont ensuite exploitées par le schéma d'allocation des ressources afin d'allouer les ressources de manière appropriée pour les différents services. Le schéma d'allocation des ressources est basé sur l'optimisation de Lyapunov, et il n'est exécuté que lorsque l'allocation des ressources est requise, ce qui réduit la latence et la consommation en ressources sur chaque équipement de périphérie. La deuxième contribution de cette thèse porte sur l'allocation des ressources pour les services en périphérie. Les services sont composés par le chaînage d'un ensemble de fonctions réseau virtuelles. L'allocation des ressources pour les services consiste en la recherche d'un placement, d'un routage et d'un ordonnancement adéquat de ces fonctions réseau virtuelles. Nous proposons une solution basée sur la théorie des jeux et sur l'apprentissage automatique pour trouver un emplacement et routage convenable ainsi qu'un ordonnancement approprié de ces fonctions en périphérie du réseau. La troisième contribution de cette thèse consiste en l'allocation des ressources pour les services véhiculaires en périphérie du réseau. Dans cette contribution, les services sont migrés et déplacés sur les différentes infrastructures en périphérie pour assurer la continuité des services. Les services véhiculaires sont en particulier sensibles à la latence et liés principalement à la sûreté et à la sécurité routière. En conséquence, la migration des services véhiculaires constitue une opération complexe. Nous proposons une approche basée sur l'apprentissage par renforcement profond pour migrer de manière proactive les différents services tout en assurant leur continuité sous les contraintes de mobilité élevée

OKpi: All-KPI Network Slicing Through Efficient Resource Allocation

Networks can now process data as well as transporting it; it follows that they can support multiple services, each requiring different key performance indicators (KPIs). Because of the former, it is critical to efficiently allocate network and computing resources to provide the required services, and, because of the latter, such decisions must jointly consider all KPIs targeted by a service. Accounting for newly introduced KPIs (e.g., availability and reliability) requires tailored models and solution strategies, and has been conspicuously neglected by existing works, which are instead built around traditional metrics like throughput and latency. We fill this gap by presenting a novel methodology and resource allocation scheme, named OKpi, which enables high-quality selection of radio points of access as well as VNF (Virtual Network Function) placement and data routing, with polynomial computational complexity. OKpi accounts for all relevant KPIs required by each service, and for any available resource from the fog to the cloud. We prove several important properties of OKpi and evaluate its performance in two real-world scenarios, finding it to closely match the optimum

Joint multi-objective MEH selection and traffic path computation in 5G-MEC systems

Multi-access Edge Computing (MEC) is an emerging technology that allows to reduce the service latency and traffic congestion and to enable cloud offloading and context awareness. MEC consists in deploying computing devices, called MEC Hosts (MEHs), close to the user. Given the mobility of the user, several problems rise. The first problem is to select a MEH to run the service requested by the user. Another problem is to select the path to steer the traffic from the user to the selected MEH. The paper jointly addresses these two problems. First, the paper proposes a procedure to create a graph that is able to capture both network-layer and application-layer performance. Then, the proposed graph is used to apply the Multi-objective Dijkstra Algorithm (MDA), a technique used for multi-objective optimization problems, in order to find solutions to the addressed problems by simultaneously considering different performance metrics and constraints. To evaluate the performance of MDA, the paper implements a testbed based on AdvantEDGE and Kubernetes to migrate a VideoLAN application between two MEHs. A controller has been realized to integrate MDA with the 5G-MEC system in the testbed. The results show that MDA is able to perform the migration with a limited impact on the network performance and user experience. The lack of migration would instead lead to a severe reduction of the user experience.publishedVersio