Towards a flexible data stream analytics platform based on the GCM autonomous software component technology

International audienceBig data stream analytics platforms not only need to support performance-dictated elasticity benefiting for instance from Cloud environments. They should also support analytics that can evolve dynamically from the application viewpoint, given data nature can change so the necessary treatments on them. The benefit is that this can avoid to undeploy the current analytics, modify it off-line, redeploy the new version, and resume the analysis, missing data that arrived in the meantime. We also believe that such evolution should better be driven by autonomic behaviors whenever possible. We argue that a software component based technology, as the one we have developed so far, GCM/ProActive, can be a good fit to these needs. Using it, we present our solution, still under development, named GCM-streaming, which to our knowledge seems to be quite original

Multi-tenant hybrid cloud architecture

This paper examines the challenges associated with the multi-tenant hybrid cloud architecture and describes how this architectural approach was applied in two software development projects. The motivation for using this architectural approach is to allow developing new features on top of monolithic legacy systems – that are still in production use – but without using legacy technologies. The architectural approach considers these legacy systems as master systems that can be extended with multi-tenant cloud-based add-on applications. In general, legacy systems are run in customer-operated environments, whereas add-on applications can be deployed to cloud platforms. It is thus imperative to have a means connectivity between these environments over the internet. The technology stack used within the scope of this thesis is limited to the offering of the .NET Core ecosystem and Microsoft Azure. In the first part of the thesis work, a literature review was carried out. The literature review focused on the challenges associated with the architectural approach, and as a result, a list of challenges was formed. This list was utilized in the software development projects of the second part of the thesis. It should be noted that there were very few high-quality papers available focusing exactly on the multi-tenant hybrid cloud architecture, so, in the end, source material for the review was searched separately for multi-tenant and for hybrid cloud design challenges. This factor is noted in the evaluation of the review. In the second part of the thesis work, the architectural approach was applied in two software development projects. Goals were set for the architectural approach: the add-on applications should be developed with modern technology stacks; their delivery should be automated; their subscription should be straightforward for customer organizations and they should leverage multi-tenant resource sharing. In the first project a data quality management tool was developed on top of a legacy dealership management system. Due to database connectivity challenges, confidentiality of customer data and authentication requirements, the implemented solution does not fully utilize the architectural approach, as having the add-on application hosted in the customer environment was the most reasonable solution. Despite this, the add-on application was developed with a modern technology stack and its delivery is automated. The subscription process does involve certain manual steps and, if the customer infrastructure changes over time, these steps must be repeated by the developers. This decreases the scalability of the overall delivery model. In the second project a PDA application was developed on top of a legacy vehicle maintenance tire hotel system. The final implementation fully utilizes the architectural approach. Support for multi-tenancy was implemented using ASP.NET Core Dependency Injection and Finbuckle.MultiTenancy-library. Azure Relay Hybrid Connection was used for hybrid cloud connectivity between the add-on application and the master system. The delivery model incorporates the same challenges regarding subscription and customer infrastructure changes as the delivery model of the data quality management tool. However, the manual steps associated with these challenges must be performed only once per customer – not once per customer per application. In addition, the delivery model could be improved to support customer self-service governance, enabling the delegation of any customer environment installations to the customers themselves. Even further, the customer environment installation could potentially cover an entire product family. As an example, instead of just providing access for the PDA application, the installation could provide access for all vehicle maintenance family add-on applications. This would make customer environment management easier and developing new add-on applications faster

Autonomic Wireless Sensor Networks: A Systematic Literature Review

Autonomic computing (AC) is a promising approach to meet basic requirements in the design of wireless sensor networks (WSNs), and its principles can be applied to efficiently manage nodes operation and optimize network resources. Middleware for WSNs supports the implementation and basic operation of such networks. In this systematic literature review (SLR) we aim to provide an overview of existing WSN middleware systems that address autonomic properties. The main goal is to identify which development approaches of AC are used for designing WSN middleware system, which allow the self-management of WSN. Another goal is finding out which interactions and behavior can be automated in WSN components. We drew the following main conclusions from the SLR results: (i) the selected studies address WSN concerns according to the self-* properties of AC, namely, self-configuration, self-healing, self-optimization, and self-protection; (ii) the selected studies use different approaches for managing the dynamic behavior of middleware systems for WSN, such as policy-based reasoning, context-based reasoning, feedback control loops, mobile agents, model transformations, and code generation. Finally, we identified a lack of comprehensive system architecture designs that support the autonomy of sensor networking

RISKS IDENTIFICATION AND MITIGATION IN UAV APPLICATIONS DEVELOPMENT PROJECTS

With the recent advances in aircraft technologies, software, sensors, and communications, Unmanned Aerial Vehicles (UAVs) can offer a wide range of applications. UAVs can play important roles in applications, such as search and rescue, situation awareness in natural disasters, environmental monitoring, and perimeter surveillance. Developing UAV applications involves integrating hardware, software, sensors, and communication components with the UAV’s base system. UAV applications development projects are complex because of the various development stages and the integration complexity of high component. This research addresses the business and technical challenges encountered by UAV applications development and Project Management (PM). It identifies the risks associated with UAV applications development and compares various risk mitigation and management techniques that can be used. The study also investigates the role of Knowledge Management (KM) in reducing and managing risks. Furthermore, this study proposes a KM framework that reduces risks in UAV applications development projects. In addition, the proposed framework relies on KM and text mining techniques to enhance the efficiency of executing these projects

Uma solução de implantação auto-adaptativa para plataformas Android

Orientador: Cecília Mary Fischer RubiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os dispositivos móveis, hoje em dia, fornecem recursos semelhantes aos de um computador pessoal de uma década atrás, permitindo o desenvolvimento de aplicações complexas. Consequentemente, essas aplicações móveis podem exigir tolerar falhas em tempo de execução. No entanto, a maioria das aplicações móveis de hoje são implantados usando configurações estáticas, tornando difícil tolerar falhas durante a sua execução. Nós propomos uma infraestrutura de implantação auto-adaptativa para lidar com este problema. A nossa solução oferece um circuito autônomo que administra o modelo de configuração atual da aplicação usando um modelo de características dinâmico associado com o modelo arquitetônico da mesma. Em tempo de execução, de acordo com a seleção dinâmica de características, o modelo arquitetônico implantado na plataforma se re-configura para fornecer uma nova solução. Uma aplicação Android foi implementada utilizando a solução proposta, e durante sua execução, a disponibilidade de serviços foi alterada, de tal forma que sua configuração corrente foi dinamicamente alterada para tolerar a indisponibilidade dos serviçosAbstract: Mobile devices, nowadays, provide similar capabilities as a personal computer of a decade ago, allowing the development of complex applications. Consequently, these mobile applications may require tolerating failures at runtime. However, most of the today¿s mobile applications are deployed using static configurations, making difficult to tolerate failure during their execution. We propose an adaptive deployment infrastructure to deal with this problem. Our solution offers an autonomic loop that manages the current configuration model of the application using a dynamic feature model associated with the architectural model. During runtime, according to the dynamic feature selection, the deployed architectural model can be modified to provide a new deployment solution. An Android application was implemented using the proposed solution, and during its execution, the services availability was altered so that its current configuration was changed dynamically in order to tolerate the unavailability of servicesMestradoCiência da ComputaçãoMestre em Ciência da Computação131830/2013-9CNP

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Big continuous data: dealing with velocity by composing event streams

International audienceThe rate at which we produce data is growing steadily, thus creating even larger streams of continuously evolving data. Online news, micro-blogs, search queries are just a few examples of these continuous streams of user activities. The value of these streams relies in their freshness and relatedness to on-going events. Modern applications consuming these streams need to extract behaviour patterns that can be obtained by aggregating and mining statically and dynamically huge event histories. An event is the notification that a happening of interest has occurred. Event streams must be combined or aggregated to produce more meaningful information. By combining and aggregating them either from multiple producers, or from a single one during a given period of time, a limited set of events describing meaningful situations may be notified to consumers. Event streams with their volume and continuous production cope mainly with two of the characteristics given to Big Data by the 5V’s model: volume & velocity. Techniques such as complex pattern detection, event correlation, event aggregation, event mining and stream processing, have been used for composing events. Nevertheless, to the best of our knowledge, few approaches integrate different composition techniques (online and post-mortem) for dealing with Big Data velocity. This chapter gives an analytical overview of event stream processing and composition approaches: complex event languages, services and event querying systems on distributed logs. Our analysis underlines the challenges introduced by Big Data velocity and volume and use them as reference for identifying the scope and limitations of results stemming from different disciplines: networks, distributed systems, stream databases, event composition services, and data mining on traces