Model
Digital Document
Publisher
Florida Atlantic University
Description
Data centers’ mission critical nature, significant power consumption, and increasing reliance on them for storing digital information, have created a need to monitor and manage these facilities. Metrics are a key part of this effort to raise flags that lead to optimization of resource utilization. While existing metrics have contributed to improvements regarding data center efficiency, they are very specific and overlook important aspects such as the overall performance and the risks to which the data center is exposed. With several variables affecting performance, there is an urgent need for new and improved metrics, capable to provide a holistic understanding of the data center behavior.
This research proposes a novel framework using a multidimensional approach for a new family of data center metrics. Performance is examined across four different subdimensions: productivity, efficiency, sustainability, and operations. Risk associated with each of those sub-dimensions is contemplated. External risks are introduced, namely site risk, as another dimension of the metrics. Results from metrics across all sub-dimensions can be normalized to the same scale and incorporated in one graph, which simplifies visualization and reporting. This research also explores theoretical modeling of data center components using a cyber-physical systems lens to estimate and predict different variables including key performance indicators. Data center simulation models are deployed in MATLAB and Simulink to assess data centers under certain a-priori known conditions. The results of the simulations, with different workloads and IT resources, show quality of service as well as power, airflow and energy parameters. Ultimately, this research describes how key parameters associated with data center infrastructure and information technology equipment can be monitored in real-time across an entire facility using low-power wireless sensors. Real-time data collection may contribute in calibrating and validating models. The new family of data center metrics gives a more comprehensive and evidence-based view of issues affecting data centers, highlights areas where mitigating actions can be implemented, and allows reexamining their overall behavior. It can help to standardize a process that evolves into a best practice for evaluating data centers, comparing them to each other, and improving grounds for decision-making.
This research proposes a novel framework using a multidimensional approach for a new family of data center metrics. Performance is examined across four different subdimensions: productivity, efficiency, sustainability, and operations. Risk associated with each of those sub-dimensions is contemplated. External risks are introduced, namely site risk, as another dimension of the metrics. Results from metrics across all sub-dimensions can be normalized to the same scale and incorporated in one graph, which simplifies visualization and reporting. This research also explores theoretical modeling of data center components using a cyber-physical systems lens to estimate and predict different variables including key performance indicators. Data center simulation models are deployed in MATLAB and Simulink to assess data centers under certain a-priori known conditions. The results of the simulations, with different workloads and IT resources, show quality of service as well as power, airflow and energy parameters. Ultimately, this research describes how key parameters associated with data center infrastructure and information technology equipment can be monitored in real-time across an entire facility using low-power wireless sensors. Real-time data collection may contribute in calibrating and validating models. The new family of data center metrics gives a more comprehensive and evidence-based view of issues affecting data centers, highlights areas where mitigating actions can be implemented, and allows reexamining their overall behavior. It can help to standardize a process that evolves into a best practice for evaluating data centers, comparing them to each other, and improving grounds for decision-making.
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