Given the vital rule of data center availability and since the inlet temperature of the IT equipment increase rapidly until reaching a certain threshold value after which IT starts throttling or shut down because of overheat during cooling system failure. Hence, it is especially important to understand failures and their effects. This study presented experimental investigation and analysis of a facility-level cooling system failure scenario in which chilled water interruption introduced to the data center. Quantitative instrumentation tools including wireless technology such as wireless temperature and pressure sensors were used to measure the discrete air inlet temperature and pressure differential though cold aisle enclosure, respectively. In addition, Intelligent Platform Management Interface (IPMI) and cooling system data during failure/recovery were reported. Furthermore, the IT equipment performance and response for opened and contained environments were simulated and compared. Finally, an experiment based analysis of the Ride Through Time (RTT) of servers during chilled water interruption of the cooling infrastructure presented as well. The results showed that for all three classes of servers tested during the cooling failure, CAC helped keep the server’s cooler for longer. The containment provided a barrier between the hot and cold air streams and caused slight negative pressuremore »
Measuring Infrastructure and Community Recovery Rate Using Bayesian Methods: A Case Study of Power Systems Resilience
With the increasing frequency and severity of disasters resulting especially from natural hazards and impacting both infrastructure systems and communities, thus challenging their timely recovery, there is a strong need to prepare for more effective response and recovery. Communities have especially struggled to understand the aspects of recovery patterns for different systems and prepare accordingly. Therefore, it is essential to develop models that are able to measure and estimate the recovery trajectory for a certain community or infrastructure network given system characteristics and event information. The objective of the study is to deploy the Poisson Bayesian kernel model developed and tested in earlier work in risk analysis to measure the recovery rate of a system. In this paper, the model is implemented and tested on a resilience modeling case study of power systems. The model is validated using a comparison to other count data models such as Poisson generalized linear model and the negative binomial generalized linear model.
- Award ID(s):
- 1635717
- Publication Date:
- NSF-PAR ID:
- 10072967
- Journal Name:
- Annual European Safety and Reliability Conference
- Sponsoring Org:
- National Science Foundation
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