Conception et implémentation de systèmes résilients par une approche à composants

L'évolution des systèmes pendant leur vie opérationnelle est incontournable. Les systèmes sûrs de fonctionnement doivent évoluer pour s'adapter à des changements comme la confrontation à de nouveaux types de fautes ou la perte de ressources. L'ajout de cette dimension évolutive à la fiabilité conduit à la notion de résilience informatique. Parmi les différents aspects de la résilience, nous nous concentrons sur l'adaptativité. La sûreté de fonctionnement informatique est basée sur plusieurs moyens, dont la tolérance aux fautes à l'exécution, où l'on attache des mécanismes spécifiques (Fault Tolerance Mechanisms, FTMs) à l'application. A ce titre, l'adaptation des FTMs à l'exécution s'avère un défi pour développer des systèmes résilients. Dans la plupart des travaux de recherche existants, l'adaptation des FTMs à l'exécution est réalisée de manière préprogrammée ou se limite à faire varier quelques paramètres. Tous les FTMs envisageables doivent être connus dès le design du système et déployés et attachés à l'application dès le début. Pourtant, les changements ont des origines variées et, donc, vouloir équiper un système pour le pire scénario est impossible. Selon les observations pendant la vie opérationnelle, de nouveaux FTMs peuvent être développés hors-ligne, mais intégrés pendant l'exécution. On dénote cette capacité comme adaptation agile, par opposition à l'adaptation préprogrammée. Dans cette thèse, nous présentons une approche pour développer des systèmes sûrs de fonctionnement flexibles dont les FTMs peuvent s'adapter à l'exécution de manière agile par des modifications à grain fin pour minimiser l'impact sur l'architecture initiale. D'abord, nous proposons une classification d'un ensemble de FTMs existants basée sur des critères comme le modèle de faute, les caractéristiques de l'application et les ressources nécessaires. Ensuite, nous analysons ces FTMs et extrayons un schéma d'exécution générique identifiant leurs parties communes et leurs points de variabilité. Après, nous démontrons les bénéfices apportés par les outils et les concepts issus du domaine du génie logiciel, comme les intergiciels réflexifs à base de composants, pour développer une librairie de FTMs adaptatifs à grain fin. Nous évaluons l'agilité de l'approche et illustrons son utilité à travers deux exemples d'intégration : premièrement, dans un processus de développement dirigé par le design pour les systèmes ubiquitaires et, deuxièmement, dans un environnement pour le développement d'applications pour des réseaux de capteurs. ABSTRACT : Evolution during service life is mandatory, particularly for long-lived systems. Dependable systems, which continuously deliver trustworthy services, must evolve to accommodate changes e.g., new fault tolerance requirements or variations in available resources. The addition of this evolutionary dimension to dependability leads to the notion of resilient computing. Among the various aspects of resilience, we focus on adaptivity. Dependability relies on fault tolerant computing at runtime, applications being augmented with fault tolerance mechanisms (FTMs). As such, on-line adaptation of FTMs is a key challenge towards resilience. In related work, on-line adaption of FTMs is most often performed in a preprogrammed manner or consists in tuning some parameters. Besides, FTMs are replaced monolithically. All the envisaged FTMs must be known at design time and deployed from the beginning. However, dynamics occurs along multiple dimensions and developing a system for the worst-case scenario is impossible. According to runtime observations, new FTMs can be developed off-line but integrated on-line. We denote this ability as agile adaption, as opposed to the preprogrammed one. In this thesis, we present an approach for developing flexible fault-tolerant systems in which FTMs can be adapted at runtime in an agile manner through fine-grained modifications for minimizing impact on the initial architecture. We first propose a classification of a set of existing FTMs based on criteria such as fault model, application characteristics and necessary resources. Next, we analyze these FTMs and extract a generic execution scheme which pinpoints the common parts and the variable features between them. Then, we demonstrate the use of state-of-the-art tools and concepts from the field of software engineering, such as component-based software engineering and reflective component-based middleware, for developing a library of fine-grained adaptive FTMs. We evaluate the agility of the approach and illustrate its usability throughout two examples of integration of the library: first, in a design-driven development process for applications in pervasive computing and, second, in a toolkit for developing applications for WSNs

A highly-available and scalable microservice architecture for access management

Access management is a key aspect of providing secure services and applications in information technology. Ensuring secure access is particularly challenging in a cloud environment wherein resources are scaled dynamically. In fact keeping track of dynamic cloud instances and administering access to them requires careful coordination and mechanisms to ensure reliable operations. PrivX is a commercial offering from SSH Communications and Security Oyj that automatically scans and keeps track of the cloud instances and manages access to them. PrivX is currently built on the microservices approach, wherein the application is structured as a collection of loosely coupled services. However, PrivX requires external modules and with specific capabilities to ensure high availability. Moreover, complex scripts are required to monitor the whole system. The goal of this thesis is to make PrivX highly-available and scalable by using a container orchestration framework. To this end, we first conduct a detailed study of mostly widely used container orchestration frameworks: Kubernetes, Docker Swarm and Nomad. We then select Kubernetes based on a feature evaluation relevant to the considered scenario. We package the individual components of PrivX, including its database, into Docker containers and deploy them on a Kubernetes cluster. We also build a prototype system to demonstrate how microservices can be managed on a Kubernetes cluster. Additionally, an auto scaling tool is created to scale specific services based on predefined rules. Finally, we evaluate the service recovery time for each of the services in PrivX, both in the RPM deployment model and the prototype Kubernetes deployment model. We find that there is no significant difference in service recovery time between the two models. However, Kubernetes ensured high availability of the services. We find that Kubernetes is the preferred mode for deploying PrivX and it makes PrivX highly available and scalable

An Access Control Model for NoSQL Databases

Current development platforms are web scale, unlike recent platforms which were just network scale. There has been a rapid evolution in computing paradigm that has created the need for data storage as agile and scalable as the applications they support. Relational databases with their joins and locks influence performance in web scale systems negatively. Thus, various types of non-relational databases have emerged in recent years, commonly referred to as NoSQL databases. To fulfill the gaps created by their relational counter-part, they trade consistency and security for performance and scalability. With NoSQL databases being adopted by an increasing number of organizations, the provision of security for them has become a growing concern. This research presents a context based abstract model by extending traditional role based access control for access control in NoSQL databases. The said model evaluates and executes security policies which contain versatile access conditions against the dynamic nature of data. The goal is to devise a mechanism for a forward looking, assertive yet flexible security feature to regulate access to data in the database system that is devoid of rigid structures and consistency, namely a document based database such as MongoDB

Task Scheduling in Big Data Platforms: A Systematic Literature Review

Context: Hadoop, Spark, Storm, and Mesos are very well known frameworks in both research and industrial communities that allow expressing and processing distributed computations on massive amounts of data. Multiple scheduling algorithms have been proposed to ensure that short interactive jobs, large batch jobs, and guaranteed-capacity production jobs running on these frameworks can deliver results quickly while maintaining a high throughput. However, only a few works have examined the effectiveness of these algorithms. Objective: The Evidence-based Software Engineering (EBSE) paradigm and its core tool, i.e., the Systematic Literature Review (SLR), have been introduced to the Software Engineering community in 2004 to help researchers systematically and objectively gather and aggregate research evidences about different topics. In this paper, we conduct a SLR of task scheduling algorithms that have been proposed for big data platforms. Method: We analyse the design decisions of different scheduling models proposed in the literature for Hadoop, Spark, Storm, and Mesos over the period between 2005 and 2016. We provide a research taxonomy for succinct classification of these scheduling models. We also compare the algorithms in terms of performance, resources utilization, and failure recovery mechanisms. Results: Our searches identifies 586 studies from journals, conferences and workshops having the highest quality in this field. This SLR reports about different types of scheduling models (dynamic, constrained, and adaptive) and the main motivations behind them (including data locality, workload balancing, resources utilization, and energy efficiency). A discussion of some open issues and future challenges pertaining to improving the current studies is provided

Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results