The granular management of all assets being placed or moved within a datacenter has become highly desirable over the past several years. Important to note is that most major companies will claim to already solved the asset management needs with an array of typically disconnected and many times complex sets of tabular asset manager products. These same companies are now quietly looking for 'something else' to help get them to where they 'really' need to be...
The newest generation of asset management suites are focused on visually representing assets with a drag-and-drop approach to adds, moves and changes. These new lifecycle management suites allow equipment to be added, moved or changed in existing facilities in a highly predictable and efficient manner. Examples of these modern suites include Aperture, Altima/Netzoom, Rackwise, nLyte, Avocent, ShowRack, APC, VisualDatacenter, Raritan/dcTrack, FieldView and a handful of others. Each of these management software suites has been crafted to allow complex data centers to be visually articulated with a high degree of fidelity, identifying everything from the manufacturer, model and serial number, to the purchase date, PO number, owner’s name and physical location.
In typical scenario, the user will graphically navigate using a drill-down tool which mimics the ‘Google Earth’ model… starting with very macro views and then selectively drilling-down to progressively more detailed views of smaller areas. In each view, various operational metrics are constantly reported such as ‘power being consumed’ within the current view. Ultimately single discrete values can be displayed.
Historically, these suites have relied on ‘faceplate’ information. This faceplate information is based upon the manufacturer’s published specification for a specific given device. It is usually the maximum value. A 1U web server for instance may have a published faceplate power consumption of 450 Watts, but the actual power draw in normal operation may be a much lower 150Watts or less. This discrepancy creates the potential for huge errors and inefficiencies when planning for overall capacity and expansion opportunities.
Consequently, one of the newest customer requirements needing to be addressed by EACH of the asset management suite vendors is to add real-time metric data. The desired metric data will obviously include the value for Power consumption, but may also include less intuitive values for fans speeds, inlet and CPU temperature, CPU and RAM utilization, available disk space, etc. While these values are relatively easy to come by as an individual user of each system, many different technologies must be exercised to programmatically and remotely retrieve these values in real-time.
This is currently where many of the latest generation of visual Asset Managers struggle. While their systems are amazing at handling the visual manipulation of IT assets, moving racks and routers along floorplans and data centers, the systems are simply not built with a large enterprise in mind when it comes to gathering Real-Time metric data. Gathering metric data for 12 servers at a trade-show is very appealing, but doing the same type of metric gathering in production against 12,000 or 112,000 servers is a bigger fish to fry. To do so requires a distributed collection architecture that is purpose built to collect any and all data from any device which is network addressable.
Real-Time monitoring with OpenData is the technology that will support the replacement of these faceplate ‘theoretical’ values with actual observed values… allowing a significantly more accurate view for planning purposes. Modius' OpenData(r) is built on a fully distributed bus architecture, is firewall friendly, and can be deployed easily to provide any asset management tool's need for Real-Time monitoring. OpenData SUPPORTS rather than replaces Asset Management suites, and has been crafted with API's and Web Services interfaces to allow the OpenData gathered metric data to be CONSUMED by any number of other applications, including the current crop of Visual Asset Managements solutions. The combination of a best-of-breed visual asset management tool with a highly granular metric monitoring solution like Modius OpenData allows business costs to be much more understood and ultimately will allow existing data centers to provide significantly more capacity and increases the lifespan of the data center itself.
Topics: data center monitoring, real-time metrics, Measurements-Metrics, IT Asset Management
One of the key power usage metrics that I often find our customers requesting is Available Redundant Capacity (ARC). This metric can mean different things to different people, but in simple terms, we at Modius like to define it as the amount of IT load that can be added to a data center system as a whole without sacrificing redundancy.
When viewed from the rack, row, room, or building level (or even across a network of data centers at the enterprise level), ARC provides a simple way to answer the question: “Where can I safely add new IT equipment without overloading and potentially bringing down my facility?”
Typically, most data centers don’t calculate ARC. Instead, operators set a simple alarm threshold on the Actual Loadof each device. For example, if the power load reaches 50% on a device (or more often 40% when de-rating), then the device or the monitoring system will throw an alarm.
However, this simple approach to thresholding based on device power usage doesn’t effectively capture all the conditions of the broader power distribution system. There can be hidden capacity that allows for safe failover, even though simple device-level thresholding suggests otherwise.
The goal of system ARC is to identify where you can handle additional load without sacrificing system redundancy. To calculate ARC for power of a device in a dual-feed situation, the calculation is simply:
ARC = {Device Capacity}/2 – {Actual Load}
In most cases, the Device Capacity will be de-rated to allow for some margin. In the case of power capacity, it is common to de-rate apparent power (kVA) capacity by 80%. ARC can also be expressed in real power (kW) if you know or can estimate the power factor of the load. It is even more important to de-rate the capacity in the case kW measurements to allow for potential load problems that could degrade power factor.
Below is an ARC-based dashboard in action:
Here, the top panel shows how ARC has been calculated for 6 different data centers, along with a measure of cooling overhead. The lower panel shows the drill down for one of the sites.
When calculating the overall ARC for devices in parallel, you can add the ARCs of the individual units. For instance:
UPS A has 10 kVA ARC
UPS B has 8 kVA ARC
Together, they have 18 kVA ARC
Interestingly, it is possible to have a safely redundant system even though one of the individual devices has a negative ARC. For example:
UPS A has 3 kVA ARC
UPS B has −2 kVA ARC
The net ARC of the system is a small but safely positive 1 kVA
In this case, even though one UPS is nominally overloaded according to the simple one-device threshold, either UPS can fail without dropping any load.
Calculating system ARC from the individual device ARCs in this way assumes that the capacities of both parallel components are the same. This is most often the case, but in the rare instance that it is not, then you have to total the actual load across the devices, and compare it to the (de-rated) capacity of the smaller device. This ensures that the most-limited device can handle the entire load.
Some questions may arise when the load is imbalanced, as in the examples above. Such imbalances may arise because some of the load is not configured redundantly. Some loads also do not balance themselves between the two power paths. The ARC calculation doesn’t depend on knowing such details. Of course, any non-redundant load will be dropped if it loses its power source; however, as long as the system ARC is positive you know that any redundant load will be protected regardless of which power source is lost.
In summary, the goal of system ARC is to identify where you can handle additional load without sacrificing system redundancy. With parallel equipment, you can total the ARC of all components if they have the same capacity rating. When looking at ARC along the power chain, the correct system value will be the minimum ARC of any one set of components.
Kind regards,
Jay H. Hartley, PhD
Director of Professional Services
Jay.Hartley@Modius.com
Topics: Data-Center-Best-Practices, data center monitoring, Dr-Jay, data center capacity, data center energy efficiency, Measurements-Metrics, Capacity-Management