Autonomous monitoring framework for resource-constrained environments

Acknowledgments The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub, reference: EP/G066051/1. URL: http://www.dotrural.ac.uk/RemoteStream/Peer reviewedPublisher PD

Multi-agent framework based on smart sensors/actuators for machine tools control and monitoring

Throughout the history, the evolutions of the requirements for manufacturing equipments have depended on the changes in the customers' demands. Among the present trends in the requirements for new manufacturing equipments, there are more flexible and more reactive machines. In order to satisfy those requirements, this paper proposes a control and monitoring framework for machine tools based on smart sensor, on smart actuator and on agent concepts. The proposed control and monitoring framework achieves machine monitoring, process monitoring and adapting functions that are not usually provided by machine tool control systems. The proposed control and monitoring framework has been evaluated by the means of a simulated operative part of a machine tool. The communication between the agents is achieved thanks to an Ethernet network and CORBA protocol. The experiments (with and without cooperation between agents for accommodating) give encouraging results for implementing the proposed control framework to operational machines. Also, the cooperation between the agents of control and monitoring framework contributes to the improvement of reactivity by adapting cutting parameters to the machine and process states and to increase productivity

Monitoring in a grid cluster

The monitoring of a grid cluster (or of any piece of reasonably scaled IT infrastructure) is a key element in the robust and consistent running of that site. There are several factors which are important to the selection of a useful monitoring framework, which include ease of use, reliability, data input and output. It is critical that data can be drawn from different instrumentation packages and collected in the framework to allow for a uniform view of the running of a site. It is also very useful to allow different views and transformations of this data to allow its manipulation for different purposes, perhaps unknown at the initial time of installation. In this context, we present the findings of an investigation of the Graphite monitoring framework and its use at the ScotGrid Glasgow site. In particular, we examine the messaging system used by the framework and means to extract data from different tools, including the existing framework Ganglia which is in use at many sites, in addition to adapting and parsing data streams from external monitoring frameworks and websites

Output Accomplishment and the Design and Monitoring Framework

{Excerpt} The designand monitoring framework is a logic model for objectives oriented planning that structures the main elements in a project, highlighting linkages between intended inputs, planned activities, and expected results. Logic models (results frameworks) neither guarantee a good project (or program) design nor replace other instruments of project management. But they help to analyze problems; identify desired outcomes; establish a logical hierarchy of means by which the desired outcomes will be reached; identify clusters of outputs; determine how accomplishments might be monitored and evaluated, and planned and actual results compared; flag the assumptions on which a project is based and the associated risks; summarize a project in a standard format; build consensus with stakeholders; and create ownership of the project

Monitoring sanitation and hygiene in the 2030 agenda for sustainable development: A review through the lens of human rights

International monitoring of drinking water and sanitation has been jointly carried out by WHO and UNICEF through their Joint Monitoring Programme (JMP). With the end of the Millennium Development Goals (MDGs) era in 2015, the JMP has proposed a post-2015 framework for integrated monitoring of water and sanitation targets included in the Sustainable Development Goal no. 6. This article discusses how each element of the proposed sanitation target and corresponding indicators can be understood from a human rights perspective. Building on the MDGs, and although some of the weaknesses and gaps persist, the discussion suggests that the post-2015 proposal is a step forward towards a monitoring framework where human rights elements related to sanitation are effectively promoted. In addition, to support the interpretation and implementation of the normative content of human rights obligations related to sanitation, the study proposes a reduced set of easy-to-assess indicators to measure the normative criteria of this right, which are then grouped in a multidimensional framework to describe increasing levels of sanitation service. To do this, the study combines literature review and specific local experience from three case studies. It is shown that the proposed monitoring tools, namely the indicators and the multidimensional indicator framework, provide guidance on monitoring the human right to sanitation. In doing so, they might ultimately help sector stakeholders in the realization of this right.Peer ReviewedPostprint (published version

A Security Monitoring Framework For Virtualization Based HEP Infrastructures

High Energy Physics (HEP) distributed computing infrastructures require automatic tools to monitor, analyze and react to potential security incidents. These tools should collect and inspect data such as resource consumption, logs and sequence of system calls for detecting anomalies that indicate the presence of a malicious agent. They should also be able to perform automated reactions to attacks without administrator intervention. We describe a novel framework that accomplishes these requirements, with a proof of concept implementation for the ALICE experiment at CERN. We show how we achieve a fully virtualized environment that improves the security by isolating services and Jobs without a significant performance impact. We also describe a collected dataset for Machine Learning based Intrusion Prevention and Detection Systems on Grid computing. This dataset is composed of resource consumption measurements (such as CPU, RAM and network traffic), logfiles from operating system services, and system call data collected from production Jobs running in an ALICE Grid test site and a big set of malware. This malware was collected from security research sites. Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.Comment: Proceedings of the 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco. Submitted to Journal of Physics: Conference Series (JPCS

An AIHW framework for assessing data sources for population health monitoring: working paper

This paper outlines the Australian Institute of Health and Welfare\u27s (AIHW) assessment framework for determining the suitability of specific data sources for population health monitoring. AIHW\u27s Assessment Framework When identifying potential data sources for population health monitoring, it is important to ensure they are \u27fit-for-purpose\u27. The AIHW has developed a 3-step process to assess potential data sources for population health monitoring: Step 1 collects information about the data source Step 2 identifies the potential to inform key monitoring areas Step 3 assesses the quality of the data, using a modified version of the Australian Bureau of Statistics (ABS) Data Quality Framework (ABS 2009), to determine its \u27fitness-for-purpose\u27 by establishing its utility, strengths and limitations. The assessment framework has been designed for use by the AIHW and others with an interest in assessing new data sources for use in population health monitoring. With adaptation, it may also have wider applications in other sectors or subject areas. For an example of the application of the assessment framework, see the AIHW working paper Assessment of the Australian Rheumatology Association Database for national population health monitoring (AIHW 2014a)

LIKWID Monitoring Stack: A flexible framework enabling job specific performance monitoring for the masses

System monitoring is an established tool to measure the utilization and health of HPC systems. Usually system monitoring infrastructures make no connection to job information and do not utilize hardware performance monitoring (HPM) data. To increase the efficient use of HPC systems automatic and continuous performance monitoring of jobs is an essential component. It can help to identify pathological cases, provides instant performance feedback to the users, offers initial data to judge on the optimization potential of applications and helps to build a statistical foundation about application specific system usage. The LIKWID monitoring stack is a modular framework build on top of the LIKWID tools library. It aims on enabling job specific performance monitoring using HPM data, system metrics and application-level data for small to medium sized commodity clusters. Moreover, it is designed to integrate in existing monitoring infrastructures to speed up the change from pure system monitoring to job-aware monitoring.Comment: 4 pages, 4 figures. Accepted for HPCMASPA 2017, the Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications, held in conjunction with IEEE Cluster 2017, Honolulu, HI, September 5, 201