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As Google has demonstrated, applying machine learning to understand heat patterns and fine-tune the data center cooling system for maximum efficiency is a sound data center use case for machine learning. But AdeptDC, the software startup applying machine learning to data center management, thinks it can be even more effective if it takes into account more than just cooling, or even power.
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Startup AdeptDC applies machine learning to data centers’ biggest source of inefficiency. Does your data center actually need all the cooling capacity it has installed? If it’s like most data centers, it does not. That both cooling and power capacity in most data centers are significantly over-provisioned is not a secret.
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Acronyms abound in IT, and data center discourse is certainly no exception. In this article, I'll discuss PUE (Power Usage Effectiveness), OPEX (operating expenses), and RAI (Resource Allocation Index). In my recent article about PUE, I wrote that data-center designers are less than satisfied with PUE as an accurate measure of how efficiently a data center uses electricity.
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Usage based pricing model is one solution for optimizing data center operation. To realize that goal, a data center’s resource supply and demand sides should match as closely as possible and for this purpose a metric, namely resource allocation index (RAI), to measure the matching between demand and supply sides of data center resources, has been proposed by Rajat Ghosh of Georgia Tech.
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One of the reasons energy conservation is such a hot button issue in the data center these days is that no one has a clear idea how to assess the situation. To be sure, metrics like PUE (Power Usage Effectiveness) are a step in the right direction, but even its backers will admit that it is not a perfect solution and should not even be used to compare one facility against another.
Read More
As Google has demonstrated, applying machine learning to understand heat patterns and fine-tune the data center cooling system for maximum efficiency is a sound data center use case for machine learning. But AdeptDC, the software startup applying machine learning to data center management, thinks it can be even more effective if it takes into account more than just cooling, or even power.
Read More
Startup AdeptDC applies machine learning to data centers’ biggest source of inefficiency. Does your data center actually need all the cooling capacity it has installed? If it’s like most data centers, it does not. That both cooling and power capacity in most data centers are significantly over-provisioned is not a secret.
Read More
Acronyms abound in IT, and data center discourse is certainly no exception. In this article, I'll discuss PUE (Power Usage Effectiveness), OPEX (operating expenses), and RAI (Resource Allocation Index). In my recent article about PUE, I wrote that data-center designers are less than satisfied with PUE as an accurate measure of how efficiently a data center uses electricity.
Read More
Usage based pricing model is one solution for optimizing data center operation. To realize that goal, a data center’s resource supply and demand sides should match as closely as possible and for this purpose a metric, namely resource allocation index (RAI), to measure the matching between demand and supply sides of data center resources, has been proposed by Rajat Ghosh of Georgia Tech.
Read More
One of the reasons energy conservation is such a hot button issue in the data center these days is that no one has a clear idea how to assess the situation. To be sure, metrics like PUE (Power Usage Effectiveness) are a step in the right direction, but even its backers will admit that it is not a perfect solution and should not even be used to compare one facility against another.
Read More
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