A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin

Mobility-aware mechanisms for fog node discovery and selection

The recent development of delay-sensitive applications has led to the emergence of the fog computing paradigm. Within this paradigm, computation nodes present at the edge of the network can act as fog nodes (FNs) capable of processing users' tasks, thus resulting in latency reductions compared to the existing cloud-based execution model. In order to realize the full potential of fog computing, new research questions have arised, mainly due to the dynamic and heterogeneous fog computing context. This thesis focuses on the following questions in particular: How can a user detect the presence of a nearby FN? How should a user on the move adapt its FN discovery strategy, according to its changing context? How should an FN be selected , in the case of user mobility and FN mobility? These questions will be addressed throughout the different contributions of this thesis. The first contribution consists in proposing a discovery solution allowing a user to become aware of the existence of a nearby FN. Using our solution, the FN advertizes its presence using custom WiFi beacons, which will be detected by the user via a scan process. An implementation of this approach has been developed and its evaluation results have shown that it results in a non-negligible energy consumption given its use of WiFi. This has led to our second contribution, which aims at improving the WiFi scan performed in our discovery approach, especially in the case of user mobility. At a first stage, this improvement consisted in embedding information about the topology of the FNs in the beacons the user receives from previous FNs. We have shown that by adapting the scan behavior based on this information, considerable energy savings can be achieved, while guaranteeing a high discovery rate. However, as this approach is associated with a restrictive FN topology structure, we proposed a different alternative, at a second stage. This alternative leverages the history of cellular context information as an indicator allowing the user to infer whether an FN may be present in its current location. If so, the scan will be enabled. Otherwise, it is disabled. The simulation results comparing different classification algorithms have shown that a sequence-based model, such as a hidden-Markov model is able to effectively predict the FN presence in the current user location. While the previous approaches have focused on a sparse FN deployment, our third contribution considers a high density of FNs. Consequently, as there are multiple nearby FNs that can process the user's tasks, it is important to derive a suitable FN selection strategy. This strategy should consider the time-varying set of FNs caused by the user's mobility. Besides, it should minimize the number of switches from one FN to another, in order to maintain a good quality of service. With these considerations in mind, we have shown that an adaptive greedy approach, that selects an FN having a good-enough delay estimate, achieves the best results. Finally, unlike the previous contribution, where the focus has been on FN selection when the user is mobile, our final contribution deals with mobile vehicular FNs (VFNs). Given the mobility of such VFNs, it is important to make the most of their resources, since they are only available for a short time at a given area. So, we propose that, in order to select an appropriate VFN for a given task, a reference roadside unit (RSU) responsible for task assignment can use advice from a neighbor RSU. This advice consists in the VFN that will result in the lowest delay for the current task, based on the experience of the neighbor RSU. The results have shown that, using the provided advice, the reference RSU can observe significant delay reductions. All in all, the proposed contributions have addressed various problems that may arise in a fog computing context and the obtained results can be used to guide the development of the building blocks of future fog computing solutions.El recent desenvolupament d'aplicacions IoT ha comportat l'aparició del paradigma de fog computing. Dins d'aquest paradigma, els nodes de càlcul presents a la vora de la xarxa poden actuar com a “fog nodes'' (FN) capaços de processar les tasques dels usuaris, produint així reduccions de latència en comparació amb el model d'execució basat en núvol. Per assolir tot el potencial del fog computing, han sorgit noves qüestions de recerca, principalment a causa del context dinàmic i heterogeni de fog computing. Aquesta tesi se centra especialment en les qüestions següents: Com pot un usuari detectar la presència d'un FN? Com hauria d’adaptar un usuari en moviment la seva estratègia de descobriment de FN, segons el seu context? Com s’ha de seleccionar un FN, en el cas de la mobilitat dels usuaris i la mobilitat FN? Aquestes preguntes s’abordaran al llarg de les diferents aportacions d’aquesta tesi. La primera contribució consisteix a proposar una solució de descobriment que permeti a l'usuari detectar l’existència d’un FN proper. Mitjançant la nostra solució, un FN anuncia la seva presència mitjançant beacons Wi-Fi personalitzats, que seran detectats per l'usuari mitjançant un procés d’exploració. S'ha desenvolupat una implementació d'aquest enfocament i els seus resultats d’avaluació han demostrat que resulta en un consum d'energia menyspreable donat el seu ús del Wi-Fi. Això ha suposat la nostra segona contribució, que té com a objectiu millorar l’exploració Wi-Fi, especialment en el cas de la mobilitat dels usuaris. En una primera fase, aquesta millora va consistir a incorporar informació sobre la topologia dels FN en les beacons que rep l'usuari dels FN anteriors. Hem demostrat que mitjançant l'adaptació del comportament d'escaneig basat en aquesta informació es pot aconseguir un estalvi considerable d’energia, alhora que es garanteix un índex elevat de descobriment. Tanmateix, com aquest enfocament s'associa a una estructura de topologia FN restrictiva, vam proposar una alternativa diferent, en una segona etapa. Aquesta alternativa aprofita la història de la informació del context cel·lular com a indicador que permet a l'usuari deduir si un FN pot estar present en la seva ubicació. En cas afirmatiu, l'exploració estarà habilitada. Els resultats de la simulació comparant diferents algoritmes de classificació han demostrat que un model basat en seqüències, com un model HMM, és capaç de predir eficaçment la presència de FNs a la ubicació actual de l'usuari. Si bé els enfocaments anteriors s’han centrat en un desplegament escàs de FNs, la nostra tercera contribució considera una alta densitat d'FNs. En conseqüència, com que hi ha múltiples FNs propers que poden processar les tasques de l'usuari, és important derivar una estratègia de selecció de FN adequada. Aquesta estratègia hauria de tenir en compte el conjunt variable de temps causat per la mobilitat de l'usuari. A més, hauria de minimitzar el nombre de canvis d'un FN a un altre, per mantenir una bona qualitat del servei. Tenint en compte aquestes consideracions, hem demostrat que un enfocament codiciós adaptatiu, que selecciona un FN amb una estimació de retard suficient, aconsegueix els millors resultats. Finalment, a diferència de l'aportació anterior, on l'atenció s'ha fixat en la selecció d'FN quan l'usuari és mòbil, la nostra contribució final tracta sobre les FNs per a vehicles mòbils (VFNs). Tenint en compte la mobilitat d’aquests VFNs, és important aprofitar al màxim els seus recursos, ja que només estan disponibles per a un temps curt. Així doncs, proposem que, per seleccionar un VFN adequat per a una tasca, una unitat RSU responsable de l'assignació de tasques pot utilitzar consells d'un RSU veí. Aquest consell consisteix en escollir el VFN que suposarà el menor retard de la tasca actual, en funció de l’experiència del RSU veí. Els resultats han demostrat que ..

VNE solution for network differentiated QoS and security requirements: from the perspective of deep reinforcement learning

The rapid development and deployment of network services has brought a series of challenges to researchers. On the one hand, the needs of Internet end users/applications reflect the characteristics of travel alienation, and they pursue different perspectives of service quality. On the other hand, with the explosive growth of information in the era of big data, a lot of private information is stored in the network. End users/applications naturally start to pay attention to network security. In order to solve the requirements of differentiated quality of service (QoS) and security, this paper proposes a virtual network embedding (VNE) algorithm based on deep reinforcement learning (DRL), aiming at the CPU, bandwidth, delay and security attributes of substrate network. DRL agent is trained in the network environment constructed by the above attributes. The purpose is to deduce the mapping probability of each substrate node and map the virtual node according to this probability. Finally, the breadth first strategy (BFS) is used to map the virtual links. In the experimental stage, the algorithm based on DRL is compared with other representative algorithms in three aspects: long term average revenue, long term revenue consumption ratio and acceptance rate. The results show that the algorithm proposed in this paper has achieved good experimental results, which proves that the algorithm can be effectively applied to solve the end user/application differentiated QoS and security requirements

A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions

Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A

A Cognitive Routing framework for Self-Organised Knowledge Defined Networks

This study investigates the applicability of machine learning methods to the routing protocols for achieving rapid convergence in self-organized knowledge-defined networks. The research explores the constituents of the Self-Organized Networking (SON) paradigm for 5G and beyond, aiming to design a routing protocol that complies with the SON requirements. Further, it also exploits a contemporary discipline called Knowledge-Defined Networking (KDN) to extend the routing capability by calculating the “Most Reliable” path than the shortest one. The research identifies the potential key areas and possible techniques to meet the objectives by surveying the state-of-the-art of the relevant fields, such as QoS aware routing, Hybrid SDN architectures, intelligent routing models, and service migration techniques. The design phase focuses primarily on the mathematical modelling of the routing problem and approaches the solution by optimizing at the structural level. The work contributes Stochastic Temporal Edge Normalization (STEN) technique which fuses link and node utilization for cost calculation; MRoute, a hybrid routing algorithm for SDN that leverages STEN to provide constant-time convergence; Most Reliable Route First (MRRF) that uses a Recurrent Neural Network (RNN) to approximate route-reliability as the metric of MRRF. Additionally, the research outcomes include a cross-platform SDN Integration framework (SDN-SIM) and a secure migration technique for containerized services in a Multi-access Edge Computing environment using Distributed Ledger Technology. The research work now eyes the development of 6G standards and its compliance with Industry-5.0 for enhancing the abilities of the present outcomes in the light of Deep Reinforcement Learning and Quantum Computing

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

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications