Data Center Monitoring Research Articles

Arduino and Nagios integration for monitoring

The data centre at the Galician Institute of High Energy Physics (IGFAE) of the Santiago de Compostela University (USC) is a computing cluster with about 150 nodes and 1250 cores that hosts the LHCb Tiers 2 and 3. In this small data centre, and of course in similar or bigger ones, it is very important to keep optimal conditions of temperature, humidity and pressure. Therefore, it is a necessity to monitor the environment and be able to trigger alarms when operating outside the recommended settings.There are currently many tools and systems developed for data centre monitoring, but until recent years all of them were of commercial nature and expensive. In recent years there has been an increasing interest in the use of technologies based on Arduino due to its open hardware licensing and the low cost of this type of components. In this article we describe the system developed to monitor IGFAE's data centre, which integrates an Arduino controlled sensor network with the Nagios monitoring software. Sensors of several types, temperature, humidity and pressure, are connected to the Arduino board. The Nagios software is in charge of monitoring the various sensors and, with the help of Nagiosgraph, to keep track of the historic data and to produce the plots. An Arduino program, developed in house, provides the Nagios plugin with the readout of one or several sensors depending on the plugin's request. The Nagios plugin for reading the temperature sensors also broadcasts an SNMP trap when the temperature gets out of the allowed operating range.

Data center asset tracking using a mobile robot

Management and monitoring of data centers is a growing field of interest, with much current research, and the emergence of a variety of commercial products aiming to improve performance, resource utilization and energy efficiency of the computing infrastructure. Despite the large body of work on optimizing data center operations, few studies actually focus on discovering and tracking the physical layout of assets in these centers. Such asset tracking is a prerequisite to faithfully performing administration and any form of optimization that relies on physical layout characteristics. In this work, we describe an approach to completely automated asset tracking in data centers, employing a vision-based mobile robot in conjunction with an ability to manipulate the indicator LEDs in blade centers and storage arrays. Unlike previous large-scale asset-tracking methods, our approach does not require the tagging of assets (e.g., with RFID tags or barcodes), thus saving considerable expense and human labor. The approach is validated through a series of experiments in a production industrial data center.

Design and Realization of Forest Fire Monitoring System Based on Image Analysis

In this paper the overall structure of forest fire monitoring system based on image analysis is designed and realized. The system includes three subsystems, which are the fire monitoring nodes based on image analysis, the forest fire prevention data center and remote monitoring terminal. It emphasizes the software and hardware realization of the fire monitoring nodes based on image analysis, analyzes the complete process of fire alarm, address locators, on-site real-time monitoring under the coordination of the ARM system, GPRS module, GPS module and EVS100K camera module. Finally, this paper describes the process how the administrator query, manage and analyze the fire prevention data.

Lightweight and Informative Traffic Metrics for Data Center Monitoring

In recent years, thousands of commodity servers have been deployed in Internet data centers to run large scale Internet applications or cloud computing services. Given the sheer volume of data communications between servers and millions of end users, it becomes a daunting task to continuously monitor the availability, performance and security of data centers in real-time operational environments. In this paper, we propose and evaluate a lightweight and informative traffic metric, streaming frequency, for network monitoring in Internet data centers. The power-series based metric that is extracted from the aggregated IP traffic streams, not only carries temporal characteristics of data center servers, but also helps uncover traffic patterns of these servers. We show the convergence and reconstructability properties of this metric through theoretical proof and algorithm analysis. Using real data-sets collected from multiple data centers of a large Internet content provider, we demonstrate its applications in detecting unwanted traffic towards data center servers. To the best of our knowledge, this paper is the first to introduce a streaming metric with a unique reconstruction capability that could aid data center operators in network management and security monitoring.

Self-organizing sensing infrastructure for autonomic management of green datacenters

The scale and complexity of modern datacenters are growing at an alarming rate due to the rising popularity of the cloud computing paradigm as an effective means to cater to the ever increasing demand for computing and storage. The management of modern datacenters is rapidly exceeding human ability, making autonomic approaches essential. In this article, methods for acquiring thermal awareness using real-time measurements and heat and air circulation models as well as solutions for proactive autonomic datacenter management that exploit this awareness are discussed. Novel communication and coordination schemes that enable self-organization of a network of external heterogeneous sensors (e.g., thermal cameras, scalar temperature and humidity sensors, airflow meters) into a multitier sensing infrastructure capable of real-time datacenter monitoring are also presented.

Impacts of Land Use/Land Cover Change on Climate and Future Research Priorities

Several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate. Researchers need to detect LULCCs accurately at appropriate scales within a specified time period to better understand their impacts on climate and provide improved estimates of future climate. The US Climate Reference Network (USCRN) can be helpful in monitoring impacts of LULCC on near-surface atmospheric conditions, including temperature. The USCRN measures temperature, precipitation, solar radiation, and ground or skin temperature. It is recommended that the National Climatic Data Center (NCDC) and other climate monitoring agencies develop plans and seek funds to address any monitoring biases that are identified and for which detailed analyses have not been completed.