FNT EXPERT PAPER. // Data Center Efficiency AUTHOR. Energy management tools and methods for green IT compliance.

Transcription

1 FNT EXPERT PAPER AUTHOR Soeren Juul Schroeder Vice President, UK/Ireland & Nordics/Baltics FNT GmbH // Data Center Efficiency Energy management tools and methods for green IT compliance Environmental sustainability is not a sweet spot for most data centers. Exponentially growing data volume and increasing performance requirements only exacerbate the situation. Efforts to improve energy usage efficiency are usually focused on facility components, such as UPS systems, but there is a better way to reduce power consumption of white space equipment without sacrificing operational security or violating performance service level agreements. Data center planners today are met with a paradox in fulfilling their duties. Not only do they need to respect and meet the business continuity requirements for uptime and availability - which is typically answered by risk mitigation and capacity oversizing - but also more and more business efficiency requirements have recently been introduced to further blur the picture. On one hand, the business might be asking for 5 nines. This request for % uptime is best implemented by using means of redundancy around the physical infrastructure. On the other hand, the business might also be asking for a 20% reduction in power usage for the data center, to minimize costs or emphasize a business-wide environmental awareness. The later obviously have to be achieved without impacting the required uptime, ruling out the ability to cut down on overhead introduced by layers of redundancy. While the first initiative for emphasizing the over-use of energy in IT environments were focused on desktop computers and printers (through the ENERGY STAR initiative launched by the Environmental Protection Agency (EPA) and the Department of Energy in the US in 1992), the primary focus today is on the data centers themselves.

2 Mostly due to the relatively high increase in power usage over the years, the data center industry has earned a reputation for wasting an unforgivable amount of energy, which has reached the ears of proactive business executives committed to reduction of carbon footprint and business sustainability. Today many different energy efficient initiatives exist. Some launched on a volunteer basis and others dictated by government regulations, but all sharing while the vision of eliminating unnecessary waste in and around data centers. However each and every initiative taken towards green IT compliance needs to be balanced with the businesses requirement for availability and performance. Requirements for Green IT Compliance The journey towards green IT starts with a changed mindset. While the data center industry for decades was used to overprovisioning Power and Cooling installations to build a resilient infrastructure, this type of approach is no longer suitable to meet tomorrow s agenda for green IT. More focus will need to be placed on right-sizing the infrastructure to meet the current and near-future requirements of the IT installation. One of the biggest challenges in achieving this is accommodating the relatively huge variation in potential power usage that a modern data center can experience. A thought experiment for a data center containing 2500 servers of the same make and model, each with a power draw between 47W (idle) and 272W (fully utilized), will ultimately reveal a wide span of power consumption needed (between 117kW and 680kW). Most data center planners would lean towards the worst-case scenario of the 680kW plus additional power for the supporting infrastructure, which easily doubles the number. This approach realistically leads to a potentiallly significant oversizing of the supporting physical infrastructure. Over the last decade manufacturers of data center IT hardware have made tremendous progress in sub-optimizing the efficiency of each of their subsystem devices, leaving us with a much better starting point for efficiency initiatives than ever before. However even the best-optimized device might be a negative contributor to the overall energy efficiency equation, if used improperly. To understand the details behind this, please consult the graphs on the bottom outlining the difference in performance of what was considered a state-of-the-art server in 2009 and a newly released 2015 server. In the graphs it s clearly shown that where performance and power usage was very linear with the CPU loads for the 2009-model, then the server manufacture have managed to de-couple the two for the 2015-model, achieving an optimal balance of CPU performance and power draw around 70-80% CPU load for the 2015-model versus 100% CPU load for the 2009 model. Other servers will have different characteristics, i.e. peaking around 60-70% and seeing even worse performance around % CPU load. This means that a high level of awareness for each make, model and configuration of servers deployed in the data center needs to be present, in order to strike the right balance between performance and energy efficiency. With the functionality available today to initiate active power capping (the practice of limiting how much electrical power a server can consume) on servers based on modern Intel chipsets, data center planners have a new option available when rightsizing the infrastructure without introducing unnecessary risk into the operation. For the example above with the 2015-model server, a power cap could be introduced at 70% CPU load, providing a design value of 191W load, resulting Fig 1: Performance and corresponding power consumption comparison at various CPU loads between 2009-model server (right) and 2015-model server (left) (source: spec.org) 2

3 in a significant reduction to the overall requirements for the facility. Another area where manufactures have made substantial progress over the years is in optimizing the efficiency of data center facility infrastructure equipment. A modern, uninterrupted power supply (UPS), for instance, has a fairly linear efficiency (around 93-97% depending on make and model) when operating above 20-25% load. This means, that as long as the UPS is seeing a fair amount of load, there is not much to worry about in terms of running efficiently on the UPS component isolated. However, when running a resilient data center with a high level of redundancy, measures need to be taken in order to ensure a good balance of load across multiple UPSes in order to ensure efficient operation of each. With virtualization taking preference in many organizations today, a new dimension of complexity is introduced in ensuring a good balance between energy efficiency and performance due to the dynamic nature of a virtualized IT system. A common misunderstanding today is that metrics, like Power Usage Effectiveness (PUE), provide a unified method to understanding data center performance. While PUE certainly has its advantages, the very definition makes it misleading when the primary goal is striking the right balance between energy efficiency and performance. For instance a 200kW data center running at 40% load would have a fairly poor PUE by definition. One way of improving the PUE would be to add more IT equipment to get the load up, which would automatically improve PUE, but which doesn t make the data center more efficient if all that IT equipment just runs idle! Also a virtualization effort, where many servers are retired by consolidating multiple servers onto fewer virtualization host servers, will automatically improve the efficiency of the IT load, but (illustrated by an increased PUE) unless the facility equipment is equally scaled at the same time, the bottom-line efficiency improvement that was scoped will not be reached! This type of complexity drives many organizations in the direction of deploying a smart toolset like Data Center Infrastructure Management (DCIM). Only by having the full tansparency provided by a DCIM tool can the data center planner be enabled to understand the implications of any planned changes and take measures to mitigate any risk imposed. By using a DCIM tool that allows documentation of the entire lifecycle (from planning, through commissioning, into operation, and ultimately decommission) can the data center planner make fact-based decisions to avoid problematic configurations that do not support the journey towards the right efficiency or performance balance. When doing system-level data center efficiency optimizations it has proven valuable to divide the approach into 4 phases, as described below: Step 1: Perform a discovery and categorize findings The first phase focuses solely on establishing a clear picture of the current state of affairs in the data center by examining the equipment deployed and its respective performance. In this phase, an understanding of the type of equipment utilized will be established as well as an indication of performance telemetric for each server. For gathering this type of usage data, the use of innovative toolsets can be utilized with great advantage, automating the process of discovering and categorizing all IT assets. The operator therefore should be offered an intuitive interface for capturing all data points associated with each data asset. These can be physical aspects (i.e. size, weight, location) as well as operational data (like expected and actual power consumption, cooling needs,etc.) Complexity like this is why a continuing focus on device-level optimizations will never enable today s data centers to reach their ultimate potential in terms of efficiency/performance balance. Rather a shift in focus to a combined device-level and holistic system-level efficiency optimization is needed. Fig 3: Screenshot from Intel DCM: Energy Director showing power measures from a blade chassis for a 24-hour period, where the server demonstrates significant higher power draw during normal business hours To determine how long the capturing process for CPU utilization needs to run, it is necessary to apply knowledge of the type of operation the servers are involved with. For classic manufacturing for instance (repeating the same work cycles day-in and day-out) it might be appropriate to focus on a 24 hour cycle, while other lines of business might need longer cycles. If in doubt, run longer cycles. Fig 2: Screenshot of a 3D footprint with rack view showing climate capacity of a data center in FNT Command Data Center Client What will likely be found here is a vast variation of IT servers in operation, where some from a power performance point of view will be performing well (medium to high average CPU utilization), some will be performing less efficiently (medium to low average CPU utilization) and others will not be performing 3

4 Typical Power Capping Policies Policiy Usage Condition Illustration Static Policy Applying a static CPU utilization limit across one or more servers to ensure a defined power threshold is never violated. Valid if server utilization is fluctuating over time without any repeatable patterns, except never exceeding a certain level. Off-hours Policy Applying 2 different CPU utilization limits across the duration of a 24 hour period, where one covers normal business hours and the other covers offhours. Valid for operations where load is higher during normal business hours and noticeably lower during off-hours. Dynamic Policy Defining upper and lower limits and allowing a context aware system to dynamically adjust the actual setting on the server based on current compute needs. Valid if server utilization is fluctuating over time without any repeatable patterns (as described for the Static Policy), however offering a more efficient variation of it. Special Purpose Policy Defining 2 different CPU utilization limits, where one covers normal performance and the other covers a server idle state. Valid for servers with only occasional (but scheduled) performance requirements, like weekly or monthly batch jobs. Emergency Policy Defining a safe power limit that can be utilized in emergency situations. Valid for use during emergency situations, like unplanned load drop on one or more UPSes, to ensure the data center can continue to run, though at lower performance. 4

5 well at all (very low average CPU utilization). Another categorization that needs to happen is based on power usage over time. While some servers will have a clear differentiation between power usage for normal business hours versus offhours, others will be harder to categories due to more random usage. Some servers that are typically used as host servers for virtualization will likely demonstrate a more static usage of power. For both categories, the usage of both peak and average consumption data needs to be examined to avoid wrong conclusions later in the process. For instance, a server dedicated to monthly financial batch jobs might typically show up as under-performing, if only consulting average CPU consumption, while its corresponding peak value will tell a different story. Step 2: Evaluate findings and explore potential strategies With discovery and categorization defined, its time to investigate the results. While action plans can quickly be established for the group of low-performing servers which are typically decommissioned after further analysis, it is more difficult with the remaining two other groups. While it is tempting to instantly strive for virtualizing the servers in the mid-to-low performing group, it might not be the best overall choice when trying to balance efficiency motives with honoring performance requirements. The challenge is to define reasonable power capping policies for each category of servers or for individual servers in extreme cases. These policies all have to be aggressive enough to provide a noticeable bottom-line saving after deployment, but also ensuring enough slack to mitigate foreseeable risk on performance. This all comes down to exploring different potential policies for each type or category of servers in the data center. Please consult the table on page 4 for some typical power capping policies, provided for illustration purposes. Fig. 4: Screenshot from FNT Command showing the planned rack front view after reclaiming lost capacity resulting from power capping For mission critical installations, the first part of this testing might be best suited for a contained testing or staging environment outside of the production data center. However, since the worst-case scenario for applying an improper policy is a decrease in application performance, which can be detected fairly quickly, it might be appropriate to apply the policies directly to the production environment. Starting with the least aggressive policy allows for verification before moving on to more aggressive policies. During the testing period it is imperative to keep a close eye on the performance of each server with changed policies. Again, this is best done using professional toolsets which allows definition of alerting thresholds and according actions. With this capability at hand, the data center planner can easily and safely deploy each policy to servers or groups of servers, while monitoring the results along the way. While exploring different possible power capping policies, the data center planner can take advantage of understanding the potential positive or negative implications of their deployment by running individual what-if scenarios with their professional DCIM toolset. This can be done for the various options and thereby testdrive the potential outcome in a safe environment. These what-if scenarios can cover different areas of the energy and performance equation, like power, cooling, and floor space capacity. If done correctly, this could allow the data center planner to free up capacity to enable the deployment of more servers into the same rack. Step 3: Test possible strategies and determine approach With potential policies determined, it s time to start testing each and every one to determine how applicable they are in the live data center. Step 4: Tune the physical infrastructure environment A last action, after all policies have been applied, is a tuning of the physical infrastructure. To understand what can be tuned and to what level, the use of smart DCIM tools is imperative. With FNT Command, for example, the data center planner is provided with a holistic view of the data center s true power and cooling needs. Furthermore, is it possible to determine the operational capacities needed from the supporting physical infrastructure in order to support the continuing operation at minimum risk. In a modern data center the ability to engage and disengage various components of the physical infrastructure might be available, while older installations will be forced to tuning the runtime conditions of the equipment. However, no matter what the situation is, the decision-making support of the right DCIM tool will help the data center planner to mitigate the risk of committing the various changes. 5

6 Summary Achieving the best return on investment for a data center can be a daunting task of balancing IT performance requirements with energy efficiency initiatives. While the later is deemed important due to government regulations, industry compliance or plain and simple cost considerations, it seems to lose momentum when battling for attention with performance and availability concerns. In its essence, green IT is about pinpointing the areas of improvement and seeking to strike the right balance between energy efficiency initiatives and performance requirements. It is no longer a game of either-or, and with visionary DCIM tools to support the data center planners throughout the journey, it has never been easier to strive for running the most energy efficient data center, while still honoring the ever changing requirements from the business. Organizations adopting DCIM tools often see a huge boost in productivity for their data center and IT functions. This is mainly due to the increased transparency across the operation and means of easier cooperation between the various groups, making DCIM adoption a great first step into green IT compliance. Copyright (C) FNT GmbH, All rights reserved. The contents of this document is subject to copyright law. Changes, abridgments, extensions and supplements require the prior written consent from FNT GmbH, Ellwangen, Germany. Reproduction is only permitted provided that this copyright notice is retained on the reproduced document. Each publication or translation requires the prior written consent from FNT GmbH, Ellwangen, Germany. FNT GmbH Röhlinger Straße 11 // Ellwangen // Germany // Phone // Fax // info@fntsoftware.com //