Situation: A leading public sector organisation identified the need to review their server estate with a view to optimising usage and potential system consolidation.
Solution: Concurrent COMMAND was used to determine the extent to which the estate was being used efficiently and which servers could be retired or converted to virtual machines in order to reduce energy consumption.
Moderate CPU usage
Low-to-Moderate CPU usage
Low CPU usage

Case study: End-to-end data centre infrastructure management:
IT system utilisation
• 70m 2 floor space
• 38 standard racks
• Over 170 servers
Like most data centres that have grown naturally over time, the client suspected their IT systems were not operating in a very efficient manner.
As a public sector organisation, they are under considerable pressures to reduce both OPEX costs and CO2 emissions, so optimising energy use within their data centres was a high priority.
They decided to review server utilisation and consider consolidation strategies that could drive down energy costs and reduce the need for space and cooling infrastructure. To do this, they used Concurrent
COMMAND, a robust Data Centre Infrastructure Management (DCIM) tool that is able to analyse the effectiveness of their servers in detail.
Concurrent COMMAND uses protocols such as ModBus, SNMP and
IPMI in order to monitor power usage at the distribution board, rack PDU and server level – or indeed wherever hardware support for remote monitoring is available. It can also use SNMP and WMI protocols in order to obtain detailed information such as CPU, network and I/O usage by interrogating the operating system itself.
Data can be manipulated and presented in multiple ways using dashboard widgets, data centre plan and rack views, and historical graphs; both for individual devices and groups of devices. This intuitive
GUI allows the user to obtain high-level management information and then drill down to obtain the detailed and highly granular technical information that is often needed to make operational decisions.
Concurrent COMMAND was used to monitor the CPU load of over
80 Windows and Linux systems systems, each a potential target for consolidation, over the course of a week. This was replicated three times, both during and outside term time, to ensure the findings were consistent. Underperforming systems were categorised by moderate
CPU usage, low-to-moderate CPU usage and low CPU usage.
CPU usage over 24 hours by category
Moderate CPU usage
Low-to-Moderate CPU usage
Low CPU usage
The data provided by Concurrent COMMAND showed that servers in the low and low-to-moderate usage categories were still consuming large amounts of power, but performing very little useful work.
Concurrent Thinking: scalable Data Centre
Infrastructure Management (DCIM) solutions
The client was also able to review system activity data and identify trends on a weekly and daily basis. For example, in the low usage group, a peak period of daily activity was identified but beyond this many of the servers were virtually idle.
www.concurrent-thinking.com
sales@concurrent-thinking.com
2

Case study: End-to-end data centre infrastructure management:
IT system utilisation
While the use of simple metrics such as CPU usage, CPU usage per watt, or CPU usage per £ of energy are useful indicators, they do not tell the whole story.
In particular, CPUs vary enormously in terms of application performance: a three year old CPU is likely to be significantly less efficient than a state-of-the-art CPU and a modern server may have four or eight times as many CPU cores. For this reason, it is useful to combine information about particular servers from Concurrent COMMAND’s built-in asset database in order to make more meaningful comparisons.
In this study, publicly available benchmarks figures were used and assigned to groups of servers of a particular type and manufacturer within the asset database. Normalised CPU usage metrics were then compared, surprisingly demonstrating that the total combined load of the servers within the low and low-to-moderate usage categories equated to just 1.7 time the peak performance of a modern CPU core and yet they were consuming 3.9kW of energy.
Weekly usage for a server in the low usage category
Concurrent COMMAND also allowed the client to delve deeper into the results to further analyse the power consumption and utilisation of each individual server. With this information, they were able to identify peaks and trends in utilisation.
Daily usage chart of one server
Concurrent Thinking: scalable Data Centre
Infrastructure Management (DCIM) solutions
The above graph shows CPU load in blue and power used in red.
This information allowed the client to identify major performance spikes, which could most likely be attributed to specific tasks such as a scheduled virus check or system backup. www.concurrent-thinking.com
sales@concurrent-thinking.com
3

Case study: End-to-end data centre infrastructure management:
IT system utilisation
The client was able to use the information collated by Concurrent
COMMAND to make decisions on how to best optimise their data centre assets.
Additional investigations into the role and workload of each machine are required before any action is taken, including their potential roles in fail-over and disaster recovery. However, through the use of Concurrent
COMMAND, it is now clear that many of the servers in the low and lowmoderate usage categories could be converted to virtual machines or retired.
Furthermore, with the detailed historical information provided by
Concurrent COMMAND, the requirements of individual virtual machines and the servers that will be needed can be accurately scoped.
In a best case scenario, with a combined peak load of less than 10 modern CPU cores and an average of 6 modern CPU cores, it is possible that all the servers in the two low-usage categories could be replaced as virtual machines on a single modern server.
This would significantly reduce the overall power needed to run these services from circa 9.3kW to 0.3kW, saving circa 30% of the power used by all the IT equipment in the data centre.
Such a reduction would also have a knock-on saving with respect to cooling requirements, resulting in a total annual saving of £14,000.
When combined with savings made as part of a previous project to optimise stand-alone cooling costs, as detailed in a corresponding case study, the total potential annual saving is nearly £20,000 or 35% of the initial total energy cost.
Concurrent Thinking: scalable Data Centre
Infrastructure Management (DCIM) solutions
To find out more about Concurrent COMMAND or to request a demo, contact us on sales@concurrent-thinking.com
.
www.concurrent-thinking.com
sales@concurrent-thinking.com
4