Towards a proper service placement in combined Fog-to-Cloud (F2C) architectures

The Internet of Things (IoT) has empowered the development of a plethora of new services, fueled by the deployment of devices located at the edge, providing multiple capabilities in terms of connectivity as well as in data collection and processing. With the inception of the Fog Computing paradigm, aimed at diminishing the distance between edge-devices and the IT premises running IoT services, the perceived service latency and even the security risks can be reduced, while simultaneously optimizing the network usage. When put together, Fog and Cloud computing (recently coined as fog-to-cloud, F2C) can be used to maximize the advantages of future computer systems, with the whole greater than the sum of individual parts. However, the specifics associated with cloud and fog resource models require new strategies to manage the mapping of novel IoT services into the suitable resources. Despite few proposals for service offloading between fog and cloud systems are slowly gaining momentum in the research community, many issues in service placement, both when the service is ready to be executed admitted as well as when the service is offloaded from Cloud to Fog, and vice-versa, are new and largely unsolved. In this paper, we provide some insights into the relevant features about service placement in F2C scenarios, highlighting main challenges in current systems towards the deployment of the next-generation IoT servicesPostprint (author's final draft

Evaluating the benefits of combined and continuous Fog-to-Cloud architectures

The need to extend the features of Cloud computing to the edge of the network has fueled the development of new computing architectures, such as Fog computing. When put together, the combined and continuous use of fog and cloud computing, lays the foundation for a new and highly heterogeneous computing ecosystem, making the most out of both, cloud and fog. Incipient research efforts are devoted to propose a management architecture to properly manage such combination of resources, such as the reference architecture proposed by the OpenFog Consortium or the recent Fog-to-Cloud (F2C). In this paper, we pay attention to such a combined ecosystem and particularly evaluate the potential benefits of F2C in dynamic scenarios, considering computing resources mobility and different traffic patterns. By means of extensive simulations we specifically study the aspects of service response time, network bandwidth occupancy, power consumption and service disruption probability. The results indicate that a combined fog-to-cloud architecture brings significant performance benefits in comparison with the traditional standalone Cloud, e.g., over 50% reduction in terms of power consumption.Preprin

A survey of communication protocols for internet of things and related challenges of fog and cloud computing integration

The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This article surveys e application layer communication protocols to fulfill the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the article first briefly presents potential protocol candidates, including request-reply and publish-subscribe protocols. After that, the article surveys these protocols based on their main characteristics, as well as the main performance issues, including latency, energy consumption, and network throughput. These findings are thereafter used to place the protocols in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their choice, interoperability, and wider system integration. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.Peer ReviewedPostprint (author's final draft

Adaptive learning-based resource management strategy in fog-to-cloud

Technology in the twenty-first century is rapidly developing and driving us into a new smart computing world, and emerging lots of new computing architectures. Fog-to-Cloud (F2C) is among one of them, which emerges to ensure the commitment for bringing the higher computing facilities near to the edge of the network and also help the large-scale computing system to be more intelligent. As the F2C is in its infantile state, therefore one of the biggest challenges for this computing paradigm is to efficiently manage the computing resources. Mainly, to address this challenge, in this work, we have given our sole interest for designing the initial architectural framework to build a proper, adaptive and efficient resource management mechanism in F2C. F2C has been proposed as a combined, coordinated and hierarchical computing platform, where a vast number of heterogeneous computing devices are participating. Notably, their versatility creates a massive challenge for effectively handling them. Even following any large-scale smart computing system, it can easily recognize that various kind of services is served for different purposes. Significantly, every service corresponds with the various tasks, which have different resource requirements. So, knowing the characteristics of participating devices and system offered services is giving advantages to build effective and resource management mechanism in F2C-enabled system. Considering these facts, initially, we have given our intense focus for identifying and defining the taxonomic model for all the participating devices and system involved services-tasks. In any F2C-enabled system consists of a large number of small Internet-of-Things (IoTs) and generating a continuous and colossal amount of sensing-data by capturing various environmental events. Notably, this sensing-data is one of the key ingredients for various smart services which have been offered by the F2C-enabled system. Besides that, resource statistical information is also playing a crucial role, for efficiently providing the services among the system consumers. Continuous monitoring of participating devices generates a massive amount of resource statistical information in the F2C-enabled system. Notably, having this information, it becomes much easier to know the device's availability and suitability for executing some tasks to offer some services. Therefore, ensuring better service facilities for any latency-sensitive services, it is essential to securely distribute the sensing-data and resource statistical information over the network. Considering these matters, we also proposed and designed a secure and distributed database framework for effectively and securely distribute the data over the network. To build an advanced and smarter system is necessarily required an effective mechanism for the utilization of system resources. Typically, the utilization and resource handling process mainly depend on the resource selection and allocation mechanism. The prediction of resources (e.g., RAM, CPU, Disk, etc.) usage and performance (i.e., in terms of task execution time) helps the selection and allocation process. Thus, adopting the machine learning (ML) techniques is much more useful for designing an advanced and sophisticated resource allocation mechanism in the F2C-enabled system. Adopting and performing the ML techniques in F2C-enabled system is a challenging task. Especially, the overall diversification and many other issues pose a massive challenge for successfully performing the ML techniques in any F2C-enabled system. Therefore, we have proposed and designed two different possible architectural schemas for performing the ML techniques in the F2C-enabled system to achieve an adaptive, advance and sophisticated resource management mechanism in the F2C-enabled system. Our proposals are the initial footmarks for designing the overall architectural framework for resource management mechanism in F2C-enabled system.La tecnologia del segle XXI avança ràpidament i ens condueix cap a un nou món intel·ligent, creant nous models d'arquitectures informàtiques. Fog-to-Cloud (F2C) és un d’ells, i sorgeix per garantir el compromís d’acostar les instal·lacions informàtiques a prop de la xarxa i també ajudar el sistema informàtic a gran escala a ser més intel·ligent. Com que el F2C es troba en un estat preliminar, un dels majors reptes d’aquest paradigma tecnològic és gestionar eficientment els recursos informàtics. Per fer front a aquest repte, en aquest treball hem centrat el nostre interès en dissenyar un marc arquitectònic per construir un mecanisme de gestió de recursos adequat, adaptatiu i eficient a F2C.F2C ha estat concebut com una plataforma informàtica combinada, coordinada i jeràrquica, on participen un gran nombre de dispositius heterogenis. La seva versatilitat planteja un gran repte per gestionar-los de manera eficaç. Els serveis que s'hi executen consten de diverses tasques, que tenen requisits de recursos diferents. Per tant, conèixer les característiques dels dispositius participants i dels serveis que ofereix el sistema és un requisit per dissenyar mecanismes eficaços i de gestió de recursos en un sistema habilitat per F2C. Tenint en compte aquests fets, inicialment ens hem centrat en identificar i definir el model taxonòmic per a tots els dispositius i sistemes implicats en l'execució de tasques de serveis. Qualsevol sistema habilitat per F2C inclou en un gran nombre de dispositius petits i connectats (conegut com a Internet of Things, o IoT) que generen una quantitat contínua i colossal de dades de detecció capturant diversos events ambientals. Aquestes dades són un dels ingredients clau per a diversos serveis intel·ligents que ofereix F2C. A més, el seguiment continu dels dispositius participants genera igualment una gran quantitat d'informació estadística. En particular, en tenir aquesta informació, es fa molt més fàcil conèixer la disponibilitat i la idoneïtat dels dispositius per executar algunes tasques i oferir alguns serveis. Per tant, per garantir millors serveis sensibles a la latència, és essencial distribuir de manera equilibrada i segura la informació estadística per la xarxa. Tenint en compte aquests assumptes, també hem proposat i dissenyat un entorn de base de dades segura i distribuïda per gestionar de manera eficaç i segura les dades a la xarxa. Per construir un sistema avançat i intel·ligent es necessita un mecanisme eficaç per a la gestió de l'ús dels recursos del sistema. Normalment, el procés d’utilització i manipulació de recursos depèn principalment del mecanisme de selecció i assignació de recursos. La predicció de l’ús i el rendiment de recursos (per exemple, RAM, CPU, disc, etc.) en termes de temps d’execució de tasques ajuda al procés de selecció i assignació. Adoptar les tècniques d’aprenentatge automàtic (conegut com a Machine Learning, o ML) és molt útil per dissenyar un mecanisme d’assignació de recursos avançat i sofisticat en el sistema habilitat per F2C. L’adopció i la realització de tècniques de ML en un sistema F2C és una tasca complexa. Especialment, la diversificació general i molts altres problemes plantegen un gran repte per realitzar amb èxit les tècniques de ML. Per tant, en aquesta recerca hem proposat i dissenyat dos possibles esquemes arquitectònics diferents per realitzar tècniques de ML en el sistema habilitat per F2C per aconseguir un mecanisme de gestió de recursos adaptatiu, avançat i sofisticat en un sistema F2C. Les nostres propostes són els primers passos per dissenyar un marc arquitectònic general per al mecanisme de gestió de recursos en un sistema habilitat per F2C.Postprint (published version

Mechanisms for service-oriented resource allocation in IoT

Albeit several IoT applications have been recently deployed in several fields, including environment and industry monitoring, Smart Home, Smart Hospital and Smart Agriculture, current deployments are mostly host-oriented, which is undoubtedly limiting the attained benefits brought up by IoT. Indeed, future IoT applications shall benefit from service-oriented communications, where the communication establishment between end-points is not dependent on prior knowledge of the host devices in charge of providing the service execution. Rather, an end-user service execution request is mapped into the most suitable resources able to provide the requested service. Furthermore, this model is a key enabler for the design of future services in Smart Cities, e-Health, Intelligent Transportation Systems, among other smart scenarios. Recognized the benefits of this model in future applications, considerable research effort must be devoted for addressing several challenges yet unsolved, such as the ones brought up by the high dynamicity and heterogeneity inherent to these scenarios. In fact, service-oriented communication requires an updated view of available resources, mapping service requests into the most suitable resources taking several constraints and requirements into account, resilience provisioning, QoS-aware service allocation, just to name a few. This thesis aims at proposing and evaluating mechanisms for efficient resource allocation in service-oriented IoT scenarios through the employment of two distinct baseline technologies. In the first approach, the so-called Path Computation Element (PCE), designed to decouple the host-oriented routing function from GMPLS switches in a centralized element, is extended to the service-oriented PCE (S-PCE) architecture, where a service identifier (SID) is used to identify the service required by an end-user. In this approach, the service request is mapped to one or a set of resources by a 2-steps mapping scheme that enables both selection of suitable resources according to request and resources characteristics, and avoidance of service disruption due to possible changes on resources¿ location. In the meantime, the inception of fog computing, as an extension of the cloud computing concept, leveraging idle computing resources at the edge of the network through their organization as highly virtualized micro data centers (MDC) enabled the reduction on the network latency observed by services launched at edge devices, further reducing the traffic at the core network and the energy consumption by network and cloud data center equipment, besides other benefits. Envisioning the benefits of the distributed and coordinated employment of both fog and cloud resources, the Fog-to-Cloud (F2C) architecture has been recently proposed, further empowering the distributed allocation of services into the most suitable resources, be it in cloud, fog or both. Since future IoT applications shall present strict demands that may be satisfied through a combined fog-cloud solution, aligned to the F2C architecture, the second approach for the service-oriented resource allocation, considered in this thesis, aims at providing QoS-aware resource allocation through the deployment of a hierarchical F2C topology, where resource are logically distributed into layers providing distinct characteristics in terms of network latency, disruption probability, IT power, etc. Therefore, distinct strategies for service distribution in F2C architectures, taking into consideration features such as service transmission delay, energy consumption and network load. Concerning the need for failure recovery mechanisms, distinct demands of heterogeneous services are considered in order to assess distinct strategies for allocation of protection resources in the F2C hierarchy. In addition, the impact of the layered control topology on the efficient allocation of resources in F2C is further evaluated. Finally, avenues for future work are presented.Aunque son ya varias las aplicaciones que se han desarrollado en el área de IoT, especialmente en el campo ambiental, Smart Home o Smart Health, las implementaciones actuales son en su mayoría ¿host-oriented¿, lo que sin duda limita sus potenciales beneficios. Una posible estrategia para reducir esos efectos negativos se centra en que las futuras aplicaciones se beneficien de las comunicaciones orientadas a servicios, ¿service-oriented¿, donde el establecimiento de comunicación entre puntos finales no depende del conocimiento previo de los hosts a cargo de proporcionar la ejecución del servicio. En este escenario, una solicitud de ejecución de servicio se asigna a los recursos más adecuados capaces de proporcionar el servicio solicitado. Este modelo se considera clave para el despliegue de futuros servicios en Smart Cities, e-Health, Intelligent Transportation Systems, etc. Reconocidos los beneficios de este modelo en las aplicaciones futuras, un substancial esfuerzo de investigación es necesario para abordar varios desafíos aún no resueltos, como los surgidos por la alta dinámica y heterogeneidad inherente a estos escenarios. De hecho, la comunicación service-oriented requiere una vista actualizada de los recursos disponibles, así como la asignación de solicitudes de servicio en los recursos más adecuados teniendo en cuenta varias restricciones y requisitos. Esta tesis tiene como objetivo proponer y evaluar mecanismos para la asignación eficiente de recursos en escenarios IoT orientados a servicios a través del empleo de dos tecnologías básicas distintas. En el primer enfoque, el llamado Path Computation Element (PCE), diseñado para desacoplar la función de enrutamiento de los conmutadores GMPLS hacia un elemento centralizado, se extiende generando la arquitectura service-oriented PCE (S-PCE). En S-PCE se utiliza un identificador de servicio (SID) para identificar el servicio requerido por un usuario final, y la solicitud se asigna, bien a uno o bien a un conjunto de recursos, mediante un esquema de asignación de 2 pasos que permite la selección de los recursos adecuados, evitando la interrupción del servicio debido a posibles cambios en la ubicación de los recursos. Mientras tanto, el inicio de Fog computing, como una extensión de Cloud computing, basado conceptualmente en aprovechar la infraestructura y los recursos inactivos en el extremo de la red a través de su organización como micro data centers (MDC), ha supuesto la reducción de la latencia de la red para los servicios lanzados por dispositivos localizados en el extremo de la red, reduciendo el tráfico en el centro de la red (backbone) así como el consumo de energía, además de otros beneficios. Asumiendo las ventajas de la utilización distribuida y coordinada de los recursos fog y cloud, la arquitectura Fog-to-Cloud (F2C) ha sido recientemente propuesta, destinada a potenciar la asignación distribuida de servicios en los recursos más adecuados, sea en cloud, fog o ambos. Dado que las futuras aplicaciones IoT deben presentar demandas que podrían ser satisfechas a través de una solución alineada con la arquitectura F2C, el segundo enfoque para la asignación de recurso orientado a servicio, considerado en esta tesis, tiene como objetivo proporcionar una asignación de recursos mediante el despliegue de una topología F2C, donde los recursos se distribuyen lógicamente en capas que proporcionan características distintas en términos de latencia de red, probabilidad de interrupción, etc. Así, se proponen distintas estrategias para la distribución de servicios, teniendo en cuenta características tales como QoS y consumo de energía. Con respecto a la necesidad de mecanismos de recuperación de fallos, se evalúan distintas estrategias para la asignación de recursos de protección en la jerarquía F2C. Además, se evalúa el impacto de la topología de control en capas sobre la asignación eficiente de recursos en F2C. Finalmente, las sugerencias para trabajos futuros son presentadas

Do we all really know what a fog node is? Current trends towards an open definition

Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.Postprint (author's final draft

Addressing the Node Discovery Problem in Fog Computing

In recent years, the Internet of Things (IoT) has gained a lot of attention due to connecting various sensor devices with the cloud, in order to enable smart applications such as: smart traffic management, smart houses, and smart grids, among others. Due to the growing popularity of the IoT, the number of Internet-connected devices has increased significantly. As a result, these devices generate a huge amount of network traffic which may lead to bottlenecks, and eventually increase the communication latency with the cloud. To cope with such issues, a new computing paradigm has emerged, namely: fog computing. Fog computing enables computing that spans from the cloud to the edge of the network in order to distribute the computations of the IoT data, and to reduce the communication latency. However, fog computing is still in its infancy, and there are still related open problems. In this paper, we focus on the node discovery problem, i.e., how to add new compute nodes to a fog computing system. Moreover, we discuss how addressing this problem can have a positive impact on various aspects of fog computing, such as fault tolerance, resource heterogeneity, proximity awareness, and scalability. Finally, based on the experimental results that we produce by simulating various distributed compute nodes, we show how addressing the node discovery problem can improve the fault tolerance of a fog computing system

Optimal Placement of Micro-services Chains in a Fog Infrastructure

Fog computing emerged as a novel approach to deliver micro-services that support innovative applications. This paradigm is consistent with the modern approach to application development, that leverages the composition of small micro-services that can be combined to create value-added applications. These applications typically require the access from distributed data sources, such as sensors located in multiple geographic locations or mobile users. In such scenarios, the traditional cloud approach is not suitable because latency constraints may not be compatible with having time-critical computations occurring on a far away data-center; furthermore, the amount of data to exchange may cause high costs imposed by the cloud pricing model. A layer of fog nodes close to application consumers can host pre-processing and data aggregation tasks that can reduce the response time of latency-sensitive elaboration as well as the traffic to the cloud data-centers. However, the problem of smartly placing micro-services over fog nodes that can fulfill Service Level Agreements is far more complex than in the more controlled scenario of cloud computing, due to the heterogeneity of fog infrastructures in terms of performance of both the computing nodes and inter-node connectivity. In this paper, we tackle such problem proposing a mathematical model for the performance of complex applications deployed on a fog infrastructure. We adapt the proposed model to be used in a genetic algorithm to achieve optimized deployment decisions about the placement of micro-services chains. Our experiments prove the viability of our proposal with respect to meeting the SLA requirements in a wide set of operating conditions

Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms

In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional data center boundaries. The focal point of this continuum are cognitive devices, which have to make decisions autonomously using their on-board computation and storage capacity based on information sensed from their environment. Such devices are moving and cannot rely on fixed infrastructure elements, but instead realise on-the-fly networking and thus frequently join and leave temporal swarms. All this creates novel demands for the underlying architecture and resource management, which must bridge the gap from edge to cloud environments, while keeping the QoS parameters within required boundaries. The paper presents an initial architecture and a resource management framework for the implementation of this type of IT service provisioning.Comment: 8 pages, CCGrid 2021 Cloud2Things Worksho