Immersion cooled environmental monitoring and prediction system for the meerKAT imager

This paper reports on an immersion cooling method used to reduce power consumption in a data centre. This study involves a case study of the MeerKAT Science Processor that is responsible for the MeerKAT imaging pipeline. Immersion cooling brings a coolant into direct physical contact with the chips and the circuit board by directly immersing computing equipment into a bath of cooling fluid. According to the National Security Agency's Laboratory for Physical Sciences (LPS), who acquired and installed an oil-immersion cooling system in 2012, the use of immersion cooling means that much of the infrastructure needed for cooling a data centre can be eliminated; it can moreover reduce server failures, and is cleaner and quieter than air cooling. In this study, an oil cooled environment is created and a prototype low-cost thermal management system for the system is built and tested. This prototyped system was called the Environmental Monitoring System (EMS), and it monitors humidity and temperature of the oil-cooled environment. In this study, discrete temperature data gathered by the thermal management system is used to build a prediction program that we called the Immersion Cooling Temperature Predictor (ICTP), which predicts temperature at locations not covered by sensors. The ICTP predicted measurements using a Gaussian process model, providing estimates for non-sampled locations to help make the monitoring systems more fault tolerant. Reducing the number of sensor nodes moreover reduces installation costs, as well as space utilized and power consumed by temperature sensors. The accuracy of detecting hotspots in an immersion cooled environment using the system is also investigated. From the experiments it was found that the ICTP had a mean error of 0, 0083, standard deviation of 2, 56 and predicted standard deviation of 2, 44 for predicting hotspots

Gaussian Processes for Multi-Sensor Environmental Monitoring

Abstract-Efficiently monitoring environmental conditions across large indoor spaces (such as warehouses, factories or data centers) is an important problem with many applications. Deployment of a sensor network across the space can provide very precise readings at discrete locations. However, construction of a continuous model from this discrete sensor data is a challenge. The challenge is made harder by economic and logistical constraints that may limit the number of sensor motes in the network. The required model, therefore, must be able to interpolate sparse data and give accurate predictions at unsensed locations, as well as provide some notion of the uncertainty on those predictions. We propose a Gaussian process based model to answer both of these issues. We use Gaussian processes to model temperature and humidity distributions across an indoor space as functions of a 3-dimensional point. We study the model selection process and show that good results can be obtained, even with sparse sensor data. Deployment of a sensor network across an indoor lab provides real-world data that we use to construct an environmental model of the lab space. We seek to refine the model obtained from the initial deployment by using the uncertainty estimates provided by the Gaussian process methodology to modify sensor distribution such that each sensor is most advantageously placed. We explore multiple sensor placement techniques and experimentally validate a near-optimal criterion