Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Enabling data analytics and machine learning for 5G services within disaggregated multi-layer transport networks

Recent advances, related to the concepts of Artificial Intelligence (AI) and Machine Learning (ML) and with applications across multiple technology domains, have gathered significant attention due, in particular, to the overall performance improvement of such automated systems when compared to methods relying on human operation. Consequently, using AI/ML for managing, operating and optimizing transport networks is increasingly seen as a potential opportunity targeting, notably, large and complex environments.Such AI-assisted automated network operation is expected to facilitate innovation in multiple aspects related to the control and management of future optical networks and is a promising milestone in the evolution towards autonomous networks, where networks self-adjust parameters such as transceiver configuration.To accomplish this goal, current network control, management and orchestration systems need to enable the application of AI/ML techniques. It is arguable that Software-Defined Networking (SDN) principles, favouring centralized control deployments, featured application programming interfaces and the development of a related application ecosystem are well positioned to facilitate the progressive introduction of such techniques, starting, notably, in allowing efficient and massive monitoring and data collection.In this paper, we present the control, orchestration and management architecture designed to allow the automatic deployment of 5G services (such as ETSI NFV network services) across metropolitan networks, conceived to interface 5G access networks with elastic core optical networks at multi Tb/s. This network segment, referred to as Metro-haul, is composed of infrastructure nodes that encompass networking, storage and processing resources, which are in turn interconnected by open and disaggregated optical networks. In particular, we detail subsystems like the Monitoring and Data Analytics or the in-operation planning backend that extend current SDN based network control to account for new use cases.Peer ReviewedPostprint (author's final draft

In-operation planning in flexgrid optical core networks

New generation applications, such as cloud computing or video distribution, can run in a telecom cloud infrastructure where the datacenters (DCs) of telecom operators are integrated in their networks thus, increasing connections' dynamicity and resulting in time-varying traffic capacities, which might also entail changes in the traffic direction along the day. As a result, a flexible optical technology able to dynamically set-up variable-capacity connections, such as flexgrid, is needed. Nonetheless, network dynamicity might entail network performance degradation thus, requiring re-optimizing the network while it is in operation. This thesis is devoted to devise new algorithms to solve in-operation network planning problems aiming at enhancing the performance of optical networks and at studying their feasibility in experimental environments. In-operation network planning requires from an architecture enabling the deployment of algorithms that must be solved in stringent times. That architecture can be based on a Path Computation Element (PCE) or a Software Defined Networks controller. In this thesis, we assume the former split in a front-end PCE, in charge of provisioning paths and handling network events, and a specialized planning tool in the form of a back-end PCE responsible for solving in-operation planning problems. After the architecture to support in-operation planning is assessed, we focus on studying the following applications: 1) Spectrum fragmentation is one of the most important problems in optical networks. To alleviate it to some extent without traffic disruption, we propose a hitless spectrum defragmentation strategy. 2) Each connection affected by a failure can be recovered using multiple paths to increase traffic restorability at the cost of poor resource utilization. We propose re-optimizing the network after repairing the failure to aggregate and reroute those connections to release spectral resources. 3) We study two approaches to provide multicast services: establishing a point-to-multipoint connections at the optical layer and using multi-purpose virtual network topologies (VNT) to serve both unicast and multicast connectivity requests. 4) The telecom cloud infrastructure, enables placing contents closer to the users. Based on it, we propose a hierarchical content distribution architecture where VNTs permanently interconnect core DCs and metro DCs periodically synchronize contents to the core DCs. 5) When the capacity of the optical backbone network becomes exhausted, we propose using a planning tool with access to inventory and operation databases to periodically decide the equipment and connectivity to be installed at the minimum cost reducing capacity overprovisioning. 6) In multi-domain multi-operator scenarios, a broker on top of the optical domains can provision multi-domain connections. We propose performing intra-domain spectrum defragmentation when no contiguous spectrum can be found for a new connection request. 7) Packet nodes belonging to a VNT can collect and send incoming traffic monitoring data to a big data repository. We propose using the collected data to predict next period traffic and to adapt the VNT to future conditions. The methodology followed in this thesis consists in proposing a problem statement and/or a mathematical formulation for the problems identified and then, devising algorithms for solving them. Those algorithms are simulated and then, they are experimentally assessed in real test-beds. This thesis demonstrates the feasibility of performing in-operation planning in optical networks, shows that it enhances the performance of the network and validates the feasibility of its deployment in real networks. It shall be mentioned that part of the work reported in this thesis has been done within the framework of several research projects, namely IDEALIST (FP7-ICT-2011-8) and GEANT (238875) funded by the EC and SYNERGY (TEC2014-59995-R) funded by the MINECO.Les aplicacions de nova generació, com ara el cloud computing o la distribució de vídeo, es poden executar a infraestructures de telecom cloud (TCI) on operadors integren els seus datacenters (DC) a les seves xarxes. Aquestes aplicacions fan que incrementi tant la dinamicitat de les connexions, com la variabilitat de les seves capacitats en el temps, arribant a canviar de direcció al llarg del dia. Llavors, cal disposar de tecnologies òptiques flexibles, tals com flexgrid, que suportin aquesta dinamicitat a les connexions. Aquesta dinamicitat pot degradar el rendiment de la xarxa, obligant a re-optimitzar-la mentre és en operació. Aquesta tesis està dedicada a idear nous algorismes per a resoldre problemes de planificació sobre xarxes en operació (in-operation network planning) per millorar el rendiment de les xarxes òptiques i a estudiar la seva factibilitat en entorns experimentals. Aquests problemes requereixen d’una arquitectura que permeti desplegar algorismes que donin solucions en temps restrictius. L’arquitectura pot estar basada en un Element de Computació de Rutes (PCE) o en un controlador de Xarxes Definides per Software. En aquesta tesis, assumim un PCE principal encarregat d’aprovisionar rutes i gestionar esdeveniments de la xarxa, i una eina de planificació especialitzada en forma de PCE de suport per resoldre problemes d’in-operation planning. Un cop validada l’arquitectura que dona suport a in-operation planning, estudiarem les següents aplicacions: 1) La fragmentació d’espectre és un dels principals problemes a les xarxes òptiques. Proposem reduir-la en certa mesura, fent servir una estratègia que no afecta al tràfic durant la desfragmentació. 2) Cada connexió afectada per una fallada pot ser recuperada fent servir múltiples rutes incrementant la restaurabilitat de la xarxa, tot i empitjorar-ne la utilització de recursos. Proposem re-optimitzar la xarxa després de reparar una fallada per agregar i re-enrutar aquestes connexions tractant d’alliberar recursos espectrals. 3) Estudiem dues solucions per aprovisionar serveis multicast: establir connexions punt-a-multipunt sobre la xarxa òptica i utilitzar Virtual Network Topologies (VNT) multi-propòsit per a servir peticions de connectivitat tant unicast com multicast. 4) La TCI permet mantenir els continguts a prop dels usuaris. Proposem una arquitectura jeràrquica de distribució de continguts basada en la TCI, on els DC principals s’interconnecten per mitjà de VNTs permanents i els DCs metropolitans periòdicament sincronitzen continguts amb els principals. 5) Quan la capacitat de la xarxa òptica s’exhaureix, proposem utilitzar una eina de planificació amb accés a bases de dades d’inventari i operacionals per decidir periòdicament l’equipament i connectivitats a instal·lar al mínim cost i reduir el sobre-aprovisionament de capacitat. 6) En entorns multi-domini multi-operador, un broker per sobre dels dominis òptics pot aprovisionar connexions multi-domini. Proposem aplicar desfragmentació d’espectre intra-domini quan no es pot trobar espectre contigu per a noves peticions de connexió. 7) Els nodes d’una VNT poden recollir i enviar informació de monitorització de tràfic entrant a un repositori de big data. Proposem utilitzar aquesta informació per adaptar la VNT per a futures condicions. La metodologia que hem seguit en aquesta tesis consisteix en formalitzar matemàticament els problemes un cop aquests son identificats i, després, idear algorismes per a resoldre’ls. Aquests algorismes son simulats i finalment validats experimentalment en entorns reals. Aquesta tesis demostra la factibilitat d’implementar mecanismes d’in-operation planning en xarxes òptiques, mostra els beneficis que aquests aporten i valida la seva aplicabilitat en xarxes reals. Part del treball presentat en aquesta tesis ha estat dut a terme en el marc dels projectes de recerca IDEALIST (FP7-ICT-2011-8) i GEANT (238875), finançats per la CE, i SYNERGY (TEC2014-59995-R), finançat per el MINECO.Postprint (published version

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Evolution towards Smart Optical Networking: Where Artificial Intelligence (AI) meets the World of Photonics

Smart optical networks are the next evolution of programmable networking and programmable automation of optical networks, with human-in-the-loop network control and management. The paper discusses this evolution and the role of Artificial Intelligence (AI)