Multi-Tenancy in Decentralised IoT

International audience—Since the Internet of Things (IoT) has become more and more important, new solutions should be proposed in order to adapt the specificities introduced by this interconnection of the physical world (Sensors and Actuators) and the public networks (The Internet). Some of these solutions use a Cloud approach. The amount of data collected by Things rises the interest of the Big-Data community. The main design chosen for the IoT is the centralisation of all data collected and a central treatment of these data. But another approach is to decentralise the data processing, in order to dramatically lighten the network and limit the exchange to a reduced set of semantic messages. This decentralised architecture has assets in term of confidentiality, data ownership and energy saving. But then, how to share things among users, and keep the control? If computing is done on each object, how a user can integrate public objects in its own application, as these objects are used by some other users? How to organise access to the sensors and actuators provided by these objects? This paper proposes an architecture that gives multi-tenant capability to IoT decentralised applications, in which users are using and sharing their objects. A generic architecture is described, and integrated in our IoT platform as an example

Evaluation of Big Data Platforms for Industrial Process Data

When the number of IoT devices, as well as human activities on the Internet, has increased fast in recent years, data generated has also witnessed an exponential growth in volume. Therefore, various frameworks and software such as Cassandra, Hive, and Spark have been developed to store and explore this massive amount of data. In particular, the waves of Big Data have also reached the industrial businesses. As the number of sensors installed in machines and mills significantly increases, log data is generated from these devices in higher frequencies and enormously complex calculations are applied to this data. The thesis is aimed at evaluating how effectively the current Big Data frameworks and tools manipulate industrial Big Data, especially process data. After surveying several techniques and potential frameworks and tools, the thesis focuses on building a prototype of a data pipeline. The prototype must satisfy a set of use cases. The data pipeline contains several components including Spark, Impala, and Sqoop. Also, it uses Parquet as the file format and stores the Parquet files in S3. Several experiments were also conducted in AWS, to validate the requirements in the use cases. The workload used for these tests was around 690 GBs of Parquet files. This amount of data includes one million channels, divided into one thousand groups, and the data sampling rate was one data point per second. The results of the experiments show that the performance of current big data frameworks may fulfill the performance requirements and the features in the use cases and industrial businesses in general

Service-Oriented Ad Hoc Grid Computing

Subject of this thesis are the design and implementation of an ad hoc Grid infrastructure. The vision of an ad hoc Grid further evolves conventional service-oriented Grid systems into a more robust, more flexible and more usable environment that is still standards compliant and interoperable with other Grid systems. A lot of work in current Grid middleware systems is focused on providing transparent access to high performance computing (HPC) resources (e.g. clusters) in virtual organizations spanning multiple institutions. The ad hoc Grid vision presented in this thesis exceeds this view in combining classical Grid components with more flexible components and usage models, allowing to form an environment combining dedicated HPC-resources with a large number of personal computers forming a "Desktop Grid". Three examples from medical research, media research and mechanical engineering are presented as application scenarios for a service-oriented ad hoc Grid infrastructure. These sample applications are also used to derive requirements for the runtime environment as well as development tools for such an ad hoc Grid environment. These requirements form the basis for the design and implementation of the Marburg ad hoc Grid Environment (MAGE) and the Grid Development Tools for Eclipse (GDT). MAGE is an implementation of a WSRF-compliant Grid middleware, that satisfies the criteria for an ad hoc Grid middleware presented in the introduction to this thesis. GDT extends the popular Eclipse integrated development environment by components that support application development both for traditional service-oriented Grid middleware systems as well as ad hoc Grid infrastructures such as MAGE. These development tools represent the first fully model driven approach to Grid service development integrated with infrastructure management components in service-oriented Grid computing. This thesis is concluded by a quantitative discussion of the performance overhead imposed by the presented extensions to a service-oriented Grid middleware as well as a discussion of the qualitative improvements gained by the overall solution. The conclusion of this thesis also gives an outlook on future developments and areas for further research. One of these qualitative improvements is "hot deployment" the ability to install and remove Grid services in a running node without interrupt to other active services on the same node. Hot deployment has been introduced as a novelty in service-oriented Grid systems as a result of the research conducted for this thesis. It extends service-oriented Grid computing with a new paradigm, making installation of individual application components a functional aspect of the application. This thesis further explores the idea of using peer-to-peer (P2P networking for Grid computing by combining a general purpose P2P framework with a standard compliant Grid middleware. In previous work the application of P2P systems has been limited to replica location and use of P2P index structures for discovery purposes. The work presented in this thesis also uses P2P networking to realize seamless communication accross network barriers. Even though the web service standards have been designed for the internet, the two-way communication requirement introduced by the WSRF-standards and particularly the notification pattern is not well supported by the web service standards. This defficiency can be answered by mechanisms that are part of such general purpose P2P communication frameworks. Existing security infrastructures for Grid systems focus on protection of data during transmission and access control to individual resources or the overall Grid environment. This thesis focuses on security issues within a single node of a dynamically changing service-oriented Grid environment. To counter the security threads arising from the new capabilities of an ad hoc Grid, a number of novel isolation solutions are presented. These solutions address security issues and isolation on a fine-grained level providing a range of applicable basic mechanisms for isolation, ranging from lightweight system call interposition to complete para-virtualization of the operating systems

Energy aware software evolution for wireless sensor networks

Wireless Sensor Networks (WSNs) are subject to high levels of dynamism arising from changing environmental conditions and application requirements. Reconfiguration allows software functionality to be optimized for current environmental conditions and supports software evolution to meet variable application requirements. Contemporary software modularization approaches for WSNs allow for software evolution at various granularities; from monolithic re-flashing of OS and application functionality, through replacement of complete applications, to the reconfiguration of individual software components. As the nodes that compose a WSN must typically operate for long periods on a single battery charge, estimating the energy cost of software evolution is critical. This paper contributes a generic model for calculating the energy cost of the reconfiguration in WSN. We have embedded this model in the LooCI middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and find that (i.) our model accurately predicts the energy cost of reconfiguration and (ii.) component-based reconfiguration has a high initial cost, but provides energy savings during software evolution

A self-integration testbed for decentralized socio-technical systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities

3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)

This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization

Monitoring self-adaptive applications within edge computing frameworks: A state-of-the-art review

Recently, a promising trend has evolved from previous centralized computation to decentralized edge computing in the proximity of end-users to provide cloud applications. To ensure the Quality of Service (QoS) of such applications and Quality of Experience (QoE) for the end-users, it is necessary to employ a comprehensive monitoring approach. Requirement analysis is a key software engineering task in the whole lifecycle of applications; however, the requirements for monitoring systems within edge computing scenarios are not yet fully established. The goal of the present survey study is therefore threefold: to identify the main challenges in the field of monitoring edge computing applications that are as yet not fully solved; to present a new taxonomy of monitoring requirements for adaptive applications orchestrated upon edge computing frameworks; and to discuss and compare the use of widely-used cloud monitoring technologies to assure the performance of these applications. Our analysis shows that none of existing widely-used cloud monitoring tools yet provides an integrated monitoring solution within edge computing frameworks. Moreover, some monitoring requirements have not been thoroughly met by any of them

Contribution à la convergence d'infrastructure entre le calcul haute performance et le traitement de données à large échelle

The amount of produced data, either in the scientific community or the commercialworld, is constantly growing. The field of Big Data has emerged to handle largeamounts of data on distributed computing infrastructures. High-Performance Computing (HPC) infrastructures are traditionally used for the execution of computeintensive workloads. However, the HPC community is also facing an increasingneed to process large amounts of data derived from high definition sensors andlarge physics apparati. The convergence of the two fields -HPC and Big Data- iscurrently taking place. In fact, the HPC community already uses Big Data tools,which are not always integrated correctly, especially at the level of the file systemand the Resource and Job Management System (RJMS).In order to understand how we can leverage HPC clusters for Big Data usage, andwhat are the challenges for the HPC infrastructures, we have studied multipleaspects of the convergence: We initially provide a survey on the software provisioning methods, with a focus on data-intensive applications. We contribute a newRJMS collaboration technique called BeBiDa which is based on 50 lines of codewhereas similar solutions use at least 1000 times more. We evaluate this mechanism on real conditions and in simulated environment with our simulator Batsim.Furthermore, we provide extensions to Batsim to support I/O, and showcase thedevelopments of a generic file system model along with a Big Data applicationmodel. This allows us to complement BeBiDa real conditions experiments withsimulations while enabling us to study file system dimensioning and trade-offs.All the experiments and analysis of this work have been done with reproducibilityin mind. Based on this experience, we propose to integrate the developmentworkflow and data analysis in the reproducibility mindset, and give feedback onour experiences with a list of best practices.RésuméLa quantité de données produites, que ce soit dans la communauté scientifiqueou commerciale, est en croissance constante. Le domaine du Big Data a émergéface au traitement de grandes quantités de données sur les infrastructures informatiques distribuées. Les infrastructures de calcul haute performance (HPC) sont traditionnellement utilisées pour l’exécution de charges de travail intensives en calcul. Cependant, la communauté HPC fait également face à un nombre croissant debesoin de traitement de grandes quantités de données dérivées de capteurs hautedéfinition et de grands appareils physique. La convergence des deux domaines-HPC et Big Data- est en cours. En fait, la communauté HPC utilise déjà des outilsBig Data, qui ne sont pas toujours correctement intégrés, en particulier au niveaudu système de fichiers ainsi que du système de gestion des ressources (RJMS).Afin de comprendre comment nous pouvons tirer parti des clusters HPC pourl’utilisation du Big Data, et quels sont les défis pour les infrastructures HPC, nousavons étudié plusieurs aspects de la convergence: nous avons d’abord proposé uneétude sur les méthodes de provisionnement logiciel, en mettant l’accent sur lesapplications utilisant beaucoup de données. Nous contribuons a l’état de l’art avecune nouvelle technique de collaboration entre RJMS appelée BeBiDa basée sur 50lignes de code alors que des solutions similaires en utilisent au moins 1000 fois plus.Nous évaluons ce mécanisme en conditions réelles et en environnement simuléavec notre simulateur Batsim. En outre, nous fournissons des extensions à Batsimpour prendre en charge les entrées/sorties et présentons le développements d’unmodèle de système de fichiers générique accompagné d’un modèle d’applicationBig Data. Cela nous permet de compléter les expériences en conditions réellesde BeBiDa en simulation tout en étudiant le dimensionnement et les différentscompromis autours des systèmes de fichiers.Toutes les expériences et analyses de ce travail ont été effectuées avec la reproductibilité à l’esprit. Sur la base de cette expérience, nous proposons d’intégrerle flux de travail du développement et de l’analyse des données dans l’esprit dela reproductibilité, et de donner un retour sur nos expériences avec une liste debonnes pratiques

Data monitoring web services in a virtual lab environment

Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 88).Environmental issues have become of prime concern due to dramatic increase in the pollution levels in all parts of the world. Underlying aquifer flow in environmentally sensitive places plays an important role in characterizing the environmental condition of the place. There is thus a pressing need for monitoring and real time analysis of hydrological data over areas of environmental interest. Coupling the emerging sensing and wireless technologies with an internet infrastructure can enable efficient data monitoring and real-time analysis of environmental conditions over an area of interest. Information gathered from various data sources regarding the change in water level and quality during various seasons can then be used to characterize trends in the physical, chemical and biological condition of the environment. Efficient real-time monitoring furnished with fast data rendering and decision-making capabilities can go a long way in monitoring Civil and Environmental Engineering infrastructure. The speed and reliability necessary for such a task can be achieved only by using a distributed infrastructure, with dedicated resources to data acquisition, archival and rendering. Distributed development technologies like DCOM, CORBA, RMI and SOAP, essentially extensions of simple RPC protocols, provide the interconnectivity between different components of such a distributed infrastructure. The present work discusses these distributed development technologies and compares them in the context of the Smartwells project.by Ashish Sadashiv Kulkarni.M.Eng