Towards data center self-diagnosis using a mobile robot

Lenchner, Jonathan; Isci, Canturk; Kephart, Jeffrey O.; Mansley, Christopher R.; Connell, Jonathan H.; McIntosh, Suzanne

doi:10.1145/1998582.1998597

Cited by 30 publications

(15 citation statements)

References 14 publications

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“…The use of a mobile robot for autonomously navigating and generating a map of a computer data center was established in [7], [8]. In addition to generating a map of the data center layout, the robot in these papers also performed a series of environmental readings which played an integral role in the thermal management of these centers.…”

Section: Related Workmentioning

confidence: 99%

Locating and tracking data center assets using active RFID tags and a mobile robot

Nelson

Santala

Lenchner

et al. 2013

2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT)

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We describe an approach to completely automated asset tracking in data centers using a vision-based mobile robot in conjunction with active RFID tags. Typically, active RFID tags are tracked using fixed readers. The granularity with which one can localize tags is based on the number of these fixed readers, each of which is not inexpensive. In large data centers, the cost of such a solution can therefore become excessive. We describe the use of a mobile robot, utilizing a single RFID reader and a variety of algorithms for locating the plethora of RFID tags one can encounter in such data centers. These approaches are validated through experiments performed in a production industrial data center.

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Section: Related Workmentioning

confidence: 99%

Locating and tracking data center assets using active RFID tags and a mobile robot

Nelson

Santala

Lenchner

et al. 2013

2013 10th International Conference and Expo on Emerging Technologies for a Smarter World (CEWIT)

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“…In [19], a fusion-based approach is developed to detect hot spots in data centers using measurements of multiple sensors. Robotic systems have also been designed to roam inside the data centers for plotting thermal map [20] and energy management [21]. However, these studies are not concerned with the real-time temperature prediction.…”

Section: Related Workmentioning

confidence: 99%

A High-Fidelity Temperature Distribution Forecasting System for Data Centers

Chen

Tan

Wang

et al. 2012

2012 IEEE 33rd Real-Time Systems Symposium

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Abstract-Data centers have become a critical computing infrastructure in the era of cloud computing. Temperature monitoring and forecasting are essential for preventing overheatinginduced server shutdowns and improving a data center's energy efficiency. This paper presents a novel cyber-physical approach for temperature forecasting in data centers, which integrates Computational Fluid Dynamics (CFD) modeling, in situ wireless sensing, and real-time data-driven prediction. To ensure the forecasting fidelity, we leverage the realistic physical thermodynamic models of CFD to generate transient temperature distribution and calibrate it using sensor feedback. Both simulated temperature distribution and sensor measurements are then used to train a real-time prediction algorithm. As a result, our approach significantly reduces the computational complexity of online temperature modeling and prediction, which enables a portable, noninvasive thermal monitoring solution that does not rely on the infrastructure of monitored data center. We extensively evaluated our system on a rack of 15 servers and a testbed of five racks and 229 servers in a production data center. Our results show that our system can predict the temperature evolution of servers with highly dynamic workloads at an average error of 0.52• C, within a duration up to 10 minutes.

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“…The robot's original purpose [2] was to automate the handpushed cart of Hamann et al [1], i.e. to map and monitor a previously unencountered data center, and feed sensor readings into commercially available data center energy monitoring and management software that could perform temperature interpolation and energy-efficiency modeling.…”

Section: A Robot-in-residencementioning

confidence: 99%

“…Finally, we have improved our original prototype [2,3] by increasing the accuracy of the interpolated temperature and humidity maps, and by incorporating a new Bayesian heuristic to speed up the robot's navigation. …”

Section: A Robot-in-residencementioning

confidence: 99%

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Demo: A robot-in-residence for data center thermal monitoring and energy efficiency management

Deland

Lenchner

Nelson

et al. 2011

Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems

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We will demonstrate a robot for data center energy management, in action, on a simulated data center floor. We shall highlight the robot's navigation, tile and obstacle classification, event scheduling and preemption capabilities, along with its ability to discover charging docks, and successfully dock with extreme precision. We shall also show simulations on real data center layouts evincing navigational efficiency gains obtained by our latest heuristic enhancements.

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Towards data center self-diagnosis using a mobile robot

Cited by 30 publications

References 14 publications

Locating and tracking data center assets using active RFID tags and a mobile robot

Locating and tracking data center assets using active RFID tags and a mobile robot

A High-Fidelity Temperature Distribution Forecasting System for Data Centers

Demo: A robot-in-residence for data center thermal monitoring and energy efficiency management

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