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

A Machine Learning framework for Sleeping Cell Detection in a Smart-city IoT Telecommunications Infrastructure

The smooth operation of largely deployed Internet of Things (IoT) applications will depend on, among other things, effective infrastructure failure detection. Access failures in wireless network Base Stations (BSs) produce a phenomenon called "sleeping cells", which can render a cell catatonic without triggering any alarms or provoking immediate effects on cell performance, making them difficult to discover. To detect this kind of failure, we propose a Machine Learning (ML) framework based on the use of Key Performance Indicator (KPI) statistics from the BS under study, as well as those of the neighboring BSs with propensity to have their performance affected by the failure. A simple way to define neighbors is to use adjacency in Voronoi diagrams. In this paper, we propose a much more realistic approach based on the nature of radio-propagation and the way devices choose the BS to which they send access requests. We gather data from large-scale simulators that use real location data for BSs and IoT devices and pose the detection problem as a supervised binary classification problem. We measure the effects on the detection performance by the size of time aggregations of the data, the level of traffic and the parameters of the neighborhood definition. The Extra Trees and Naive Bayes classifiers achieve Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) scores of 0.996 and 0.993, respectively, with False Positive Rate (FPR) under 5 %. The proposed framework holds potential for other pattern recognition tasks in smart-city wireless infrastructures, that would enable the monitoring, prediction and improvement of the Quality of Service (QoS) experienced by IoT applications.Comment: Submitted to the IEEE Access Journa

Increasing resilience of ATM networks using traffic monitoring and automated anomaly analysis

Systematic network monitoring can be the cornerstone for the dependable operation of safety-critical distributed systems. In this paper, we present our vision for informed anomaly detection through network monitoring and resilience measurements to increase the operators' visibility of ATM communication networks. We raise the question of how to determine the optimal level of automation in this safety-critical context, and we present a novel passive network monitoring system that can reveal network utilisation trends and traffic patterns in diverse timescales. Using network measurements, we derive resilience metrics and visualisations to enhance the operators' knowledge of the network and traffic behaviour, and allow for network planning and provisioning based on informed what-if analysis

Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications

The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions