La aparición del Internet de las cosas (IoT) ha atraído el interés de industria y academia para su aplicación en dominios intensivos, como la salud. Esta clase de aplicaciones tienen requisitos estrictos de calidad de servicio (QoS), lo que motiva el uso de paradigmas como edge o fog computing. Las redes definidas por sofware, junto a las arquitecturas de microservicios, permiten el uso de dichos paradigmas proveyendo virtualización, flexibilidad y programabilidad a las aplicaciones IoT distribuidas. Sin embargo, para cumplir los estrictos requisitos de estas aplicaciones, la QoS debe optimizarse considerando la interacción de tres dimensiones: computación, red y aplicación. En este trabajo presentamos el framework Despliegue Óptimo de Aplicaciones Distribuidas, que optimiza la localización de microservicios y recursos de red en términos de tiempo de respuesta y coste del despliegue.
Autores: Juan Luis Herrera / Jaime Galán-Jiménez / José García-Alonso / Javier Berrocal / Juan Manuel Murillo Rodríguez /
Palabras Clave: Edge Computing - Fog Computing - Internet of Things - Quality of Service - Software defined networks
The irruption of the Internet of Things (IoT) has attracted the interest of both the industry and academia for their application in intensive domains, such as healthcare. The strict Quality of Service (QoS) requirements of the next gener- ation of intensive IoT applications requires the QoS to be optimized considering the interplay of three key dimensions: computing, networking and application. This optimization requirement motivates the use of paradigms that provide vir- tualization, flexibility and programmability to IoT applications. In the com- puting dimension, paradigms such as edge or fog computing, Software-Defined Networks in the networking dimension, along with micro-services architectures for the application dimension, are suitable for QoS-strict IoT scenarios. In this work, we present a framework, named Next-gen IoT Optimization (NIoTO), that considers these three dimensions and their interplay to place micro-services and networking resources over an infrastructure, optimizing the deployment in terms of average response time and deployment cost. The evaluation of NIoTO in a healthcare case study reveals a response time speed-up of up to 5.11 and a reduction in cost of up to 9% with respect to other state-of-the-art techniques.
Autores: Juan Luis Herrera / Jaime Galán-Jiménez / Jose García-Alonso / Javier Berrocal / Juan Manuel Murillo Rodríguez /
Palabras Clave: Fog Computing - Internet of Things (IoT) - Quality of Service (QoS)
The Internet of Things (IoT) paradigm offers applications the potential of automating real-world processes. Applying IoT to intensive domains comes with strict quality of service (QoS) requirements, such as very short response times. To achieve these goals, the first option is to distribute the computational workload throughout the infrastructure (edge, fog, cloud). In addition, integration of the infrastructure with enablers such as software-defined networks (SDNs) can further improve the QoS experience, thanks to the global network view of the SDN controller and the execution of optimization algorithms. Therefore, the best placement for both the computation elements and the SDN controllers must be identified to achieve the best QoS. While it is possible to optimize the computing and networking dimensions separately, this results in a suboptimal solution. Thus, it is crucial to solve the problem in a single effort. In this work, the influence of both dimensions on the response time is analyzed in fog computing environments powered by SDNs. DADO, a framework to identify the optimal deployment for distributed applications is proposed and implemented through the application of mixed integer linear programming. An evaluation of an IIoT case study shows that our proposed framework achieves scalable deployments over topologies of different sizes and growing user bases. In fact, the achieved response times are up to 37.89% lower than those of alternative solutions and up to 15.42% shorter than those of state-of-the-art benchmarks.
Autores: Juan Luis Herrera / Jaime Galán-Jiménez / Javier Berrocal / Juan Manuel Murillo Rodríguez /
Palabras Clave: Edge Computing - Fog Computing - Internet of Things - Software Defined Network
