Michael Alan Schappert

Uncovering energy-efficiency opportunities in data centers

The combination of rapidly increasing energy use of data centers (DCs), which is triggered by dramatic increases in IT (information technology) demands, and increases in energy costs and limited energy supplies has made the energy efficiency of DCs a central concern from both a cost and a sustainability perspective. This paper describes three important technology components that address the energy consumption in DCs. First, we present a mobile measurement technology (MMT) for optimizing the space and energy efficiency of DCs. The technology encompasses the interworking of an advanced metrology technique for rapid data collection at high spatial resolution and measurement-driven modeling techniques, enabling optimal adjustments of a DC environment within a target thermal envelope. Specific example data demonstrating the effectiveness of MMT is shown. Second, the static MMT measurements obtained at high spatial resolution are complemented by and integrated with a real-time sensor network. The requirements and suitable architectures for wired and wireless sensor solutions are discussed. Third, an energy and thermal model analysis for a DC is presented that exploits both the high-spatial-resolution (but static) MMT data and the high-timeresolved (but sparse) sensor data. The combination of these two data types (static and dynamic), in conjunction with innovative modeling techniques, provides the basis for extending the MMT concept toward an interactive energy management solution.

Methods and techniques for measuring and improving data center best practices

Here we present a novel, measurement-based method for characterizing and improving the energy efficiency of a data center (DC). The technique not only yields a clear set of measurement-based best practices metrics, but also provides clear guidance to a DC manager to substantially improve the DC energy efficiency. We describe a technology which exploits the superiority of fast and massive parallel data collection using the Mobile Measurement Technology (MMT) [1] to drive towards quantitative, measurement-driven DC best practices implementation. A large representative raised-floor DC is mapped by the MMT methodology readily yielding the DC layout, very detailed 3D temperature distributions, flows and other relevant physical parameters of the specific facility. The data is used to calculate key metrics (horizontal and vertical hotspots, targeted air flow, plenum temperature, air conditioning unit utilization and flow levels). These metrics provide insights into the sources of energy inefficiencies of the current DC setup, and systematically guide DC managers to improve various best practice aspects in the specific DC. It is shown that significant energy reductions can be achieved utilizing the above described best practices methodology.

A mouse adapter for people with hand tremor

Hand tremor, which affects millions of individuals worldwide, can make it difficult or impossible to operate computers that rely on a mouse, or similar pointing device, for controlling the user interface. We describe an assistive adapter that, when inserted between the mouse and the computer, provides digital motion-smoothing filtering, rejection of inadvertent mouse button clicks, and enhanced double clicking. Because its behavior closely emulates a standard mouse, this setup is operating-system independent and requires no special software on the computer. Its assistive features are active for any application with a mouse-driven interface. In a preliminary test involving people with essential tremor, most subjects reported improvements ranging from moderate to considerable.

Robotic mapping and monitoring of data centers

We describe an inexpensive autonomous robot capable of navigating previously unseen data centers and monitoring key metrics such as air temperature1. The robot provides real-time navigation and sensor data to commercial IBM software, thereby enabling real-time generation of the data center layout, a thermal map and other visualizations of energy dynamics. Once it has mapped a data center, the robot can efficiently monitor it for hot spots and other anomalies using intelligent sampling. We demonstrate the robots effectiveness via experimental studies from two production data centers.

Acoustic contact sensor for handwritten computer input

An acoustic contact sensor for handwritten computer input is disclosed herein. The contact sensor comprises ultrasonic sending transducer means, receiving transducer means and circuit means for providing a logic signal to a computer when contact is made. The sending and receiver transducer means can either individually be coupled to a pen stylus or writing and/or display surface.

Comparison of Experimental Temperature Results With Numerical Modeling Predictions of a Real-World Compact Data Center Facility

In this paper we address the high level of validation of commercially available numerical predictive tools, namely CFD models, for data center applications. Experimental data at a real world facility is compared with computational results. Specifically, a recently developed temperature measurement tool is used to capture three dimensional temperature profiles of the facility with very fine spatial granularity. These detailed contours based on actual measurements reveal hot air recirculation patterns in the room as well as variable utilization levels of the room air-conditioning units. We compare these experimental temperature distributions measured using a novel 3D mapping tool with CFD thermal modeling results. An algorithm for generating CFD models for real world systems is proposed and demonstrated. It is found that due to the extensive inaccuracies in rapidly gathered input data as well as inherent limitations of the model, there can be significant discrepancies between predicted and actual temperatures. In addition to steady state measurements, transient data was also collected and are presented. Knowledge of the transient temperature profiles at different parts of the room allows an estimation of the temporal behavior of the data center. Transient temperature fluctuations are presented which capture the real variations in the system boundary conditions, for example the temperature of the chilled water to the air conditioning units, or the power dissipation of the servers over time.Copyright

Corrosion risk management in IT facilities

Environmental conditions in data centers determine the reliability of the IT equipment and operational uptimes. Due to continuous increase in energy usage and a trend towards sustainability, leads many data center operators to lean towards implementing energy efficiency measures. Many of todays data center are operated in a narrow temperature and relative humidity band, however these may change as ASHRAE recently expanded the limits of the temperature and relative humidity. These expansions are triggered by recent interest to implement air side economizer based cooling in data centers. For air side economized data centers besides pollutants that can be introduced unintentionally, the daily variation of temperature and relative humidity could possibly lead to condensation or corrosion. The variation of the corrosion rate due to daily changes of the indoor pollutant concentrations combined with temperature and humidity cycling are presented for a data center.

A tele-operative RMMT system facilitating the management of cooling and energy in data centers

This paper presents a tele-operative robotic mobile measurement technique for collecting temperature data in data centers. It provides highly effective data support to the analysis of data center thermal profiles, and plays an active role in improving data center cooling and energy efficiencies. With this technique, a human operator controls the navigation of the teleoperative mobile measurement platform and triggers the data acquisition operation through a wirelessly connected console. Comparing with traditional manual operation, it largely reduces the intensity of human labor and improves the operational efficiency. The effectiveness of the proposed technique has been verified through a demonstrative data scanning in a real data center environment.

Demo: Smarter data center power monitoring and management

This demonstration presents a power panel level power monitoring and management (PMM) system developed at IBM Research. The ultimate goal of this project is to develop a low-cost, high accuracy, non-intrusive and retrofittable data center power management system.