Towards the Automation of Data Networks

Una amplia variedad de empresas, corporaciones, CSPs y especialistas han enfatizado la dificultad de gestionar redes modernas, que introducen innovaciones tecnológicas de alto impacto, como cloud computing, movilidad, nuevos perfiles de tráfico, NFV, IoT, Big Data, entre otras. La automatización de redes es una metodología en la que los dispositivos de red físicos y virtuales se configuran, aprovisionan, administran y prueban automáticamente mediante software. Grandes empresas, como Cisco, Juniper, Red Hat o VMWare ofrecen soluciones propietarias de automatización de red. Además, recientemente ha habido un aumento en la cantidad de herramientas que asisten en la automatización de redes. Ambos hechos han marcado un cambio en la forma en que los administradores construyen y administran las redes. La mayoría de los grandes operadores de comunicaciones trabajan y tienden, en este sentido, a redes verdaderamente autónomas que, eventualmente, requerirán del uso intensivo de Inteligencia Artificial (IA).  Los avances en la temática muestran que tres segmentos específicos CSPs, Proveedores en la Nube, y las empresas se encuentran en diferentes etapas de madurez de la automatización. Se espera, que progresivamente, esta tendencia alcance, también, a las organizaciones de menor envergadura. Este documento presenta un estudio técnico especializado, detallado y actual sobre el estado del arte en automatización de redes, destacando las tendencias que se observan en los entornos de TI, de las empresas y operadores de comunicaciones, más involucrados en esta tecnología y, finalizando, con una discusión sobre herramientas de automatización.A wide variety of enterprises, corporations, communications service providers (CSPs), and experts have highlighted the difficulty of managing modern networks. These networks exhibit high-impact technological innovations, such as cloud computing, mobility, new traffic profiles, network functions virtualization (NFV), the Internet of things (IoT), Big Data, among others. Network automation is a methodology in which physical and virtual network devices are automatically configured, provisioned, managed, and tested using software. Large enterprises such as Cisco, Juniper, Red Hat, and VMWare offer proprietary solutions for network automation. Additionally, the number of tools assisting in network automation has recently increased. Taken together, these developments have changed the way administrators build and manage networks. In this regard, most large communications operators are now working and moving toward truly autonomous networks that will eventually require an intensive use of Artificial Intelligence (AI). Advances in the area show that three specific segments —CSPs, Cloud Providers, and Enterprises— are all at different stages of automation maturity. Over time, network automation is expected to reach smaller organizations as well. This paper presents a specialized, detailed and current technical study on the state of the art in network automation, highlighting the trends observed in information technology (IT) environments, enterprises and communications operators —which are closely involved in this technology—, and concludes with a discussion on automation tools

NETQOS policy management architecture for flexible QOS provisioning in Future Internet

This paper is focussed on the NETQOS architecture for automated QoS policy provisioning, which can be used in Future Internet scenarios by the different actors (i.e. network operators, service providers, and users) for flexible QoS configuration over combinations of mobile, fixed, sensor and broadcast networks. The NETQOS policy management architecture opens the possibility to specify QoS policies on a "business" level using ontology descriptions and policy management interfaces, which are specific to the actors. The business level policy specifications are translated by the NETQOS system into intermediate and operational QoS policies for automated QoS configuration at the managed heterogeneous network and transport entities. NETQOS allows QoS policy specification and dependency analysis considering Service Level Agreements (SLAs) between the actors, as well as automated policy provisioning and adaptation. The interaction of the NETQOS components is based on a common po licy repository. The particular focus of the paper is aimed to discuss ontology and actor oriented QoS policy specification and configuration for heterogeneous networks, as well as NETQOS QoS policy management interfaces at business level and automated translation of business QoS policies to intermediate and operational policy level

An infrastructure service recommendation system for cloud applications with real-time QoS requirement constraints

The proliferation of cloud computing has revolutionized the hosting and delivery of Internet-based application services. However, with the constant launch of new cloud services and capabilities almost every month by both big (e.g., Amazon Web Service and Microsoft Azure) and small companies (e.g., Rackspace and Ninefold), decision makers (e.g., application developers and chief information officers) are likely to be overwhelmed by choices available. The decision-making problem is further complicated due to heterogeneous service configurations and application provisioning QoS constraints. To address this hard challenge, in our previous work, we developed a semiautomated, extensible, and ontology-based approach to infrastructure service discovery and selection only based on design-time constraints (e.g., the renting cost, the data center location, the service feature, etc.). In this paper, we extend our approach to include the real-time (run-time) QoS (the end-to-end message latency and the end-to-end message throughput) in the decision-making process. The hosting of next-generation applications in the domain of online interactive gaming, large-scale sensor analytics, and real-time mobile applications on cloud services necessitates the optimization of such real-time QoS constraints for meeting service-level agreements. To this end, we present a real-time QoS-aware multicriteria decision-making technique that builds over the well-known analytic hierarchy process method. The proposed technique is applicable to selecting Infrastructure as a Service (IaaS) cloud offers, and it allows users to define multiple design-time and real-time QoS constraints or requirements. These requirements are then matched against our knowledge base to compute the possible best fit combinations of cloud services at the IaaS layer. We conducted extensive experiments to prove the feasibility of our approach

Software-Defined Cloud Computing: Architectural Elements and Open Challenges

The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi, Indi

The role of big data analytics in industrial internet of things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well