From the server rack to the storage array, the Internet of Things will stretch data center resources to the limit. Find out how to maximize and manage capacity more effectively.
Thank you to all who replied. I appreciate the information and insight.
Keep in mind...You cannot model that which you cannot measure.
In production, APM solutions tell us workload throughput and response times, but does not necessarily give resource consumption per workload.
This is where correlation reporting is our friend!
As Rob states “a workload should be defined that correlates against a measured set of resource utilization“, but identifying that key workload that correlates to utilization can be a challenge. APM can gives us A LOT of workload data, but getting actionable information from the data in reports can be a challenge. By bringing APM data into the Capacity Management solution, we can start getting some actionable information. For example, Ade’s correlation workload reports where you can select different workloads and servers that are in the same groupings and see which workloads have the strongest correlation to the individual system, or the group. This analysis proves which workloads are driving the utilization, armed with this info we can use this workload as a natural forecasting unit for the groups' resource consumption.
I can see how that could be done and presented in a report. For example, the user could get a report of which workload in the group has the strongest correlation with the group’s servers usage. Then this workload can be set as the KPI for the group and a model would use that workload’s throughput as the input. Group level and system level reports can then include that KPI along with how it correlates to resource usage. Armed with this we can model to increases in the KPI to see impact workload change on resources. An assumption is that ratio of workloads associated with the KPI will remain constant.
Sent: Wednesday, June 03, 2015 09:57
To: Lamb, Kip M
Subject: Re: - An App-Centric Capacity Planning Approach
CA Communities <https://communities.ca.com/?et=blogs.comment.created>
An App-Centric Capacity Planning Approach
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Good advice from Richard. I would add to this..
Be careful of how you define “workload”. For capacity management purposes, a workload should be defined that correlates against a measured set of resource utilizations. If you define your workloads too tightly, you may struggle to allocate the observed utilizations against your defined workloads. On the other hand, if you define your workloads too loosely, then you may miss important time-related relationships.
In general terms, I would suggest a ‘top-down’ approach to workload definition. Ask yourself whether you can start with a single workload, measured by a single throughput metric (such as number of users). If this doesn’t make sense – for example, you might have a ‘batch’ workload and an ‘online’ workload which are easily separated by time, then you can extend your model accordingly. Another example of where you can extend to multiple workloads is if you have sufficiently granular data, which may be forthcoming from an APM tool perhaps.
There are two different ways to approach this problem:
These two approach are consistent with ITIL's description of the Capacity Management sub-processes
Step 1 - Start with a tier-by-tier approach.
The result of this step is a quantitative description and evaluation of each tier in your infrastructure. This corresponds to ITIL's "Component Capacity Management". In addition, this step is required before you start looking into the more detailed applications (workload).
Step 2 - Extend to a workload-level analysis
The result of Step 2 is the start of an application profile. Note that we are extending the initial component-level analysis from Step 1. You are effectively layering applications on top of your infrastructure components. This is exactly where you want to be to start application-level capacity planning.
Based on the 80/20 rule there are twelve workloads.
How many workloads?
My application environment consists of fourteen (14) different tiers on eighty (80) different servers. Would you recommend a tier-by-tier app-centric capacity planning approach?