Capacity Planning 101

The objective of Capacity Planning is to adequately anticipate current and future capacity demand (resource consumption requirements) for a given environment. This helps to accurately evaluate demand growth, identify growth drivers and proactively trigger any procurement activities (purchase, extension, upgrade etc.).

 

Capacity planning is based primarily on two items. The first one is analyzing historical data to obtain organic consumption and growth trends. The second one is predicting the future by analyzing the pipeline of upcoming projects, taking also in consideration migrations and hardware refreshes. IT and Business must work hand-in-hand to ensure that any upcoming projects are well-known in advance.

 

The Challenges with Capacity Planning or “the way we’ve always done it”

 

Manual capacity planning by running scripts here and there, exporting data, compiling data and leveraging Excel formulas can work. However, there are limits of one’s time availability, and at the expense of not focusing into higher priority issues.

 

The time spent on manually parsing data, reconciling and reviewing can be nothing short of a huge challenge, if not a waste of time. The larger an environment grows, the larger the dataset will be, the longer it will take to prepare capacity reports. And the more manual the work is, the more it is prone to human errors.  While it’s safe to assume that any person with Excel skills and a decent set of instruction can generate capacity reports, the question remains about their accuracy. It’s also important to point out that new challenges have emerged for those who like manual work.

 

Space saving technologies like deduplication and compression have complicated things. What used to be a fairly simple calculation of linear growth based on growth trends and YoY estimates is now complicated by non-linear aspects such as compression and dedupe savings. Since both compression and deduplication ratios are dictated by the type of data as well as the specifics of the technology (see in-line vs. at-rest deduplication, as well as block size), it becomes extremely complicated to factor this into a manual calculation process. Of course, you could “guesstimate” compression and/or deduplication factors for each of your servers. But the expected savings can also fail to materialize for a variety of reasons.

 

Typical mistakes in capacity management and capacity planning involve space reclamation activities at the storage array level. Rather, the lack of  awareness and  activities on the matter. Monitoring storage consumption at the array level without relating with the way storage has been provisioned at the hypervisor level may result in discrepancies. For example, not running Thin Provisioning Block Space Reclamation (through the VMware VAAI UNMAP primitive) on VMware environments may lead some individuals to believe that a storage array is reaching critical capacity levels while in fact a large portion of the allocated blocks is no longer active and can be reclaimed.

 

Finally, in manual capacity planning, any attempt to run “What-If” scenarios (adding n number of VMs with a given usage profile for a new project) are wild guesses at best. Even while having the best intentions and focus, you are likely to end up either with an under-provisioned environment and resource pressure, or with an over-provisioned environment with idle resources. While the latter is preferable, this is still a waste of money that might’ve been invested anywhere else.

 

Capacity Planning – Doing It Right

 

As we’ve seen above, the following factors can cause incorrect capacity planning:

  • Multiple sources of data collected in different ways
  • Extremely large datasets to be processed/aggregated manually
  • Manual, simplistic data analysis
  • Key technological improvements not taken into account
  • No simple way to determine effects of a new project into infrastructure expansion plans

 

Additionally, all of the factors above are also prone to human errors.

 

Because the task of processing data manually is nearly impossible and also highly inefficient, precious allies such as Solarwinds Virtualization Manager are required to identify real-time issues, bottlenecks, potential noisy neighbors as well as wasted resources. Once these wasted resources are reclaimed, capacity planning can provide a better evaluation of the actual estimated growth in your environment.

 

Capacity planning activities are not just about looking into the future, but also about managing the environment as it is now. The link between Capacity Planning and Capacity Reclamation activities is crucial. Just as you want to keep your house tidy before planning an extension or improving it with new furniture, the same needs to be done with your virtual infrastructure.

 

Proper capacity planning should factor in the following items:

  • Central, authoritative data source (all the data is collected by a single platform)
  • Automated data aggregation and processing through software engine
  • Advanced data analysis based on historical trends and usage patterns
  • What-If scenarios engine for proper measurement of upcoming projects
  • Capacity reclamation capabilities (Managing VM sprawl)

 

Conclusion

 

Enterprises must consider whether capacity planning done “the way we’ve always done it” is adding any value to their business or rather being the Achilles heel of their IT strategy. Because of its criticality, capacity planning should not be considered as a recurring manual data collection/aggregation chore that is assigned to “people who know Excel”. Instead, it should be run as a central, authoritative function that measures current usage, informs about potential issues and provides key insights to plan future investments in time.