A survey on online monitoring approaches of computer-based systems

This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use

Wireless Sensors and Networks for Advanced Energy Management

Numerous national studies and working groups have identified low-cost, very low-power wireless sensors and networks as a critical enabling technology for increasing energy efficiency, reducing waste, and optimizing processes. Research areas for developing such sensor and network platforms include microsensor arrays, ultra-low power electronics and signal conditioning, data/control transceivers, and robust wireless networks. A review of some of the research in the following areas will be discussed: (1) Low-cost, flexible multi-sensor array platforms (CO{sub 2}, NO{sub x}, CO, humidity, NH{sub 3}, O{sub 2}, occupancy, etc.) that enable energy and emission reductions in applications such as buildings and manufacturing; (2) Modeling investments (energy usage and savings to drive capital investment decisions) and estimated uptime improvements through pervasive gathering of equipment and process health data and its effects on energy; (3) Robust, self-configuring wireless sensor networks for energy management; and (4) Quality-of-service for secure and reliable data transmission from widely distributed sensors. Wireless communications is poised to support technical innovations in the industrial community, with widespread use of wireless sensors forecasted to improve manufacturing production and energy efficiency and reduce emissions. Progress being made in wireless system components, as described in this paper, is helping bring these projected improvements to reality

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Lifeguard: Local Health Awareness for More Accurate Failure Detection

SWIM is a peer-to-peer group membership protocol with attractive scaling and robustness properties. However, slow message processing can cause SWIM to mark healthy members as failed (so called false positive failure detection), despite inclusion of a mechanism to avoid this. We identify the properties of SWIM that lead to the problem, and propose Lifeguard, a set of extensions to SWIM which consider that the local failure detector module may be at fault, via the concept of local health. We evaluate this approach in a precisely controlled environment and validate it in a real-world scenario, showing that it drastically reduces the rate of false positives. The false positive rate and detection time for true failures can be reduced simultaneously, compared to the baseline levels of SWIM

A QoS-configurable failure detection service for internet applications

International audienceUnreliable failure detectors are a basic building block of reliable distributed systems. Failure detectors are used to monitor processes of any application and provide process state information. This work presents an Internet Failure Detector Service (IFDS) for processes running in the Internet on multiple autonomous systems. The failure detection service is adaptive, and can be easily integrated into applications that require configurable QoS guarantees. The service is based on monitors which are capable of providing global process state information through a SNMP MIB. Monitors at different networks communicate across the Internet using Web Services. The system was implemented and evaluated for monitored processes running both on single LAN and on PlanetLab. Experimental results are presented, showing the performance of the detector, in particular the advantages of using the self-tuning strategies to address the requirements of multiple concurrent applications running on a dynamic environment

Support for dependable and adaptive distributed systems and applications

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2011Distributed applications executing in uncertain environments, like the Internet, need to make timing/synchrony assumptions (for instance, about the maximum message transmission delay), in order to make progress. In the case of adaptive systems these temporal bounds should be computed at runtime, using probabilistic or specifically designed ad hoc approaches, typically with the objective of improving the application performance. From a dependability perspective, however, the concern is to secure some properties on which the application can rely. This thesis addresses the problem of supporting adaptive systems and applications in stochastic environments, from a dependability perspective: maintaining the correctness of system properties after adaptation. The idea behind dependable adaptation consists in ensuring that the assumed bounds for fundamental variables (e.g., network delays) are secured with a known and constant probability. Assuming that during its lifetime a system alternates periods where its temporal behavior is well characterized (stable phases), with transition periods where a variation of the network conditions occurs (transient phases), the proposed approach is based on the following: if the environment is generically characterized in analytical terms and it is possible to detect the alternation of these stable and transient phases, then it is possible to effectively and dependably adapt applications. Based on this idea, the thesis introduces Adaptare, a framework for supporting dependable adaptation in stochastic environments. An extensive evaluation of Adaptare is provided, assessing the correctness and effectiveness of the implemented mechanisms. The results indicate that the proposed strategies and methodologies are indeed effective to support dependable adaptation of distributed systems and applications. Finally, the applicability of Adaptare is evaluated in the context of two fundamental problems in distributed systems: consensus and failure detection. The thesis proposes solutions for these problems based on modular architectures in which Adaptare is used as a middleware for dependable adaptation of assumed timeouts.Aplicações distribuídas que executam em ambientes incertos, como a Internet, baseiam-se em pressupostos sobre tempo/sincronia (por exemplo, assumem um tempo máximo para a transmissão de mensagens) a fim de assegurar progresso. No caso de sistemas adaptativos, esses limites temporais devem ser calculados em tempo de execução, usando abordagens probabilísticas ou desenhadas de forma específica e ad hoc, tipicamente visando melhorar o desempenho da aplicação. Sob o ponto de vista da confiabilidade, no entanto, o objetivo é garantir algumas propriedades nas quais a aplicação pode confiar. Esta tese aborda o problema de suportar sistemas adaptativos e aplicações que operam em ambientes estocásticos, numa perspectiva de confiabilidade: mantendo a correção das propriedades do sistema após a adaptação. A ideia da adaptação confiável consiste em garantir que os limites assumidos para variáveis fundamentais (por exemplo, latências de transmissão) são assegurados com uma probabilidade conhecida e constante. Supondo que durante a execução o sistema alterna períodos nos quais o seu comportamento temporal é bem caracterizado (fases estáveis), com períodos de transição durante os quais ocorrem variações das condições da rede (fases transientes), a abordagem proposta baseia-se no seguinte: se o ambiente é genericamente caracterizado em termos analíticos e é possível detetar a alternância entre fases estáveis e transientes, então é possível adaptar as aplicações de forma efetiva e confiável. Com base nesta ideia, a tese apresenta uma plataforma para suportar a adaptação confiável em ambientes estocásticos, denominada Adaptare. A tese contém uma extensa avaliação do Adaptare, que foi realizada para verificar a correção e eficácia dos mecanismos desenvolvidos. Os resultados indicam que as estratégias e metodologias propostas são de facto efetivas para suportar a adaptação confiável de sistemas e aplicações distribuídas. Finalmente, a aplicabilidade do Adaptare é avaliada no contexto de dois problemas fundamentais em sistemas distribuídos: consenso e deteção de falhas. A tese propõe soluções para estes problemas baseadas em arquiteturas modulares nas quais o Adaptare é usado como um middleware para a adaptação confiável de timeouts.Fundação para a Ciência e a Tecnologia (FCT