The Need for an Analytic-Driven Data Center
Everyone knows the importance of analytics in today’s modern, digital world. Without measurable data, it is difficult to make informed decisions about operations, efficiency, personnel, products, utilities, and more. Analytics is a critical component of any data center’s DCIM (data center infrastructure management) but – only if the analytics are being used.
Most data centers are collecting a wealth of information via analytics, whether they realize it or not. And, if data centers are being run effectively, the analytics are being used to optimize efficiency and maximize a data center’s potential. The way data centers run is constantly changing as the overall landscape changes, technology updates, and more consistent performance is continually demanded. Data Center Knowledge explains how metrics were analyzed and used within the data center previously and why moving towards an analytic-driven data center is so important, “Optimization, as applied to data centers, means always having the right amount of resources, to cost-effectively enable the business use of those data centers. Right resourcing means, in effect, enough to get the data center “job” done, but not so much as to waste money. Everything from enough power and floor space to enough “computes,” and everything else. Easily said, but increasingly challenging to accomplish. It used to be that one would optimize any given data center resource by measuring resource utilization; for example, how busy a CPU is, and then make a considered determination of what level was sufficiently busy to be upgraded or extended, or sufficiently non-busy to warrant consolidation. This approach was used, and useful, for everything from CPUs, memory and other server metrics, to things like power consumption, where metrics like PUE (power utilization effectiveness) were created and applied. However, these types of optimizations were always done in domain isolation – silos in effect. On the software side, pervasive virtualization, containerization, and software automation have completely changed the measurement landscape. The increasingly rich metrics embedded in server chipsets open exciting new possibilities…The closer that data centers can get to complete instrumentation of important metrics like transactional response time and throughput, the lower the overall associated costs will be to successfully deliver those business services, and the more efficient such data centers can become.”
Every data center is unique – the buildings are unique, employees are unique, companies are unique, data demands are unique, heating/cooling is unique, technology being used is unique, etc. Thus, data center analytics is crucial because it is the only way to gather the data necessary to make informed decisions. Data center analytics provide facts that simply cannot be denied about how a data center operates – good or bad. Once the analytics are assessed, most data centers will have at least one (or a few) areas in which they can make improvements. As data centers continue to evolve and adapt to modern demands, the insight that analytics provide will directly influence how a data center develops more efficient processes.
Adopting Analytics to Maximize Energy Efficiency in the Data Center
The adoption of data-driven operations within a data center will help improve operations, make data centers more efficient, and provide a wealth of other benefits. One of the most significant benefits of utilizing analytics as a tool in data centers is that you can get a clear picture of what is and what is not energy efficient within the data center. Data centers use an astonishing amount of energy and so any areas in which savings can be found can have a significant impact. In fact, by strategically using data center analytics to inform improved energy efficient practices within the data center, millions of dollars can be saved. Data Center Frontier explains why analytics are so important in maximizing data center energy efficiency, “Keeping track of how much power you are consuming and how much is available is foundational to the efficiency of any data center, but many data center managers don’t look beyond total actual active power and budgeted power capacity remaining. Trending power capacity can help you forecast more accurately, while tracking actual power by customer cabinets and a location’s total power month to month can help you manage customer billback and energy usage more accurately.”
Adopting Analytics to Maximize Operational Efficiency in the Data Center
Data center operations are constantly changing because technology, infrastructure, and personnel are constantly changing. The way to avoid confusion and maximize operational efficiency is to have a robust DCIM and that includes the use of analytics to inform decisions. Making decisions about how to update a data center based on assumptions or anecdotal evidence from the past is not the best way to move forward in a thoughtful and efficient way. First, assumptions and anecdotal evidence are rarely correct so you are already starting off on a bad foot. But also, you want hard facts about exactly what has happened in the past as well as predictive analysis that can help you be well-prepared for the future. And, with real-time analytics you do not have to wait until some arbitrary, pre-determined maintenance date to analyze data, you can see exactly what is happening the moment it is happening so that you can make in-the-moment decisions about how to adapt for data center needs.
Streamline Yet Maximize Data Centers – How?
As data gets bigger and both physical and digital demands increase in data centers, there is a simultaneous greater demand for reducing the footprint of a data center. So, how are data centers supposed to do more with less exactly? Analytics can help optimize operations, infrastructure, and energy use to do just that. Without analytics, there are often servers not in use that are being stored and even drawing on the energy resources of a data center. Rooms that are maintained at a specific temperature may be able to adjust that temperature by 1 or 2 degrees and save a significant amount of money on cooling costs. There are so many things that come to light when data center analytics are examined. TechBeacon explains how analytics can often bring to light wasteful resource allocation, “The combination of asset and usage data also enables you to identify waste. Remember those 10 million idle servers worldwide? Analytics and reporting tools can pinpoint physical servers that are running but whose usage is zero. Retiring them or reallocating them to other workloads eliminates waste and reduces spending. In addition, asset and usage data helps uncover resources that have been overallocated capacity. Organizations that are moving into virtualization without a clear understanding of the capacity requirements of the virtualized machines (VMs) may overallocate capacity to be sure there is enough capacity for the VMs and applications running on a physical server. It’s not uncommon to see a virtual server running on a powerful host even though that server is not doing heavy work and does not require the capacity that has been allocated to it. Moving it to a less powerful host in the data center frees up capacity on the more powerful host.”
Data Center Analytics Provide Transparency
A data center may be run by a manager or team of personnel but often a CIO, customer, or another individual will want to know exactly what is happening in the data center to maximize operational and energy efficiency. Without analytics, you will be left trying to explain what is happening without any real concrete evidence of your efforts. Data center analytics is a great way to enhance a data center’s transparency in their efforts to be more operationally and energy efficient. Every data center, regardless of size, must utilize analytics as an important component of their overall DCIM to maximize operational and energy efficiency moving forward.