Elasticity of data center scale is a common goal for enterprises. It allows them to scale up, scale down, and scale out their resources as needed, instead of incurring the full burden of capital expenditures in one go. The problem for us IT professionals is how to manage the elasticity of software-defined resources efficiently and effectively while helping ensure that no degradation in service delivery from saturation and error events.
What I call "IT OAR" represents actions that you need to perform in order to guide a hybrid data center through its entire lifecycle. The “O” is for optimization, as you deliver more efficacy with the resources afforded you. The “A” is for automation, as you provide more scale in the services being integrated and delivered. The “R” is for reporting the news of efficiency and effectiveness of optimization and automation actions. Combining all three provides you with the means to navigate the digital transformation era and the burden that it’s bringing to bear on IT operations.
How do you get the most out of the IT OAR? Trust but verify at your unique scale. Without the experience and expertise of a seasoned IT professional, the IT OAR can lead to paddling in circles and wasted motions. Let’s peel back the layers of optimization, automation, and reporting to help ensure successful service integration and delivery in the data center.
Optimization
Optimization in the virtual data center spans virtual data center health across resource utilization and saturation while encompassing resource capacity planning and resource elasticity. Utilization, saturation, and errors play key roles in the optimization skill. The key question is: What needs to be optimized in the virtual data center?
Similar to other IT disciplines, optimization in the virtual environment boils down to optimizing resources (i.e., doing more with less). This often produces an over-commitment of resources and the eventual contention issues that follow the saturated state. If the contention persists over an extended period of time, or comes too fast and furious, errors usually crop up.
Resource optimization starts with tuning compute (vCPUs), memory (vRAM), network, and storage. It extends to the application and its tunable properties through the hypervisor to the host and cluster.
vCPU and vRAM penalties manifest in saturation and errors, which lead to slow application performance and tickets. There are definite costs to oversizing and undersizing virtual machines (VMs). Optimization seeks to find the fine line with respect to the entire virtual data center environment.
To optimize compute cycles, look for vCPU utilization and their counters as well as processor queue length. For instance, with VMware, the CPU counters to examine are: %USED, %RDY, and %CSTP. %USED shows how much time a VM spent executing CPU cycles on the physical CPU. %RDY defines the percentage of time a VM wanted to execute, but had to wait to be scheduled by the VMKernel. %CSTP is the percentage of time that a SMP VM was ready to run, but incurred delay because of co-vCPU scheduling contention. The performance counters in Microsoft are System\Processor Queue Length, Process\% Processor Time, Processor\%Processor Time, and Thread\% Processor Time.
To optimize memory, look for memory swapping and guest level paging. For VMware, the counters are SWP/s and SWW/s; for Microsoft, the counter is pages/s. For Linux VMs, leverage vmstat and the swap counters si and so -- swap in and swap out, respectively.
Of course, a virtualization maestro needs to factor in hypervisor kernel optimization/reclamation techniques as well as the application stack and the layout of his or her virtual data center infrastructure into the optimization process.
Automation
Automation in the virtual data center spans workflows. These workflows can encompass management actions, such as provisioning or reclaiming virtual resources, setting up profiles and configurations in a one-to-many manner, and reflecting best practices in policies across the virtual data center in a consistent and scalable way.
Scripts, templates, and blueprints embody IT automation. They are created from your best practice methodology -- tried and true IT methods and processes. Unfortunately, automation itself cannot differentiate between good and bad. Therefore, automating bad IT practices will lead to unbelievable pain at scale across your virtual data centers.
To prevent that from happening, keep automation simple. First, automate at a controlled scale by following the mantra, “Do no harm to your production data center environment.” Next, monitor the automation process from start to finish to ensure that every step executes as expected. Finally, analyze the results and use your findings to make necessary adjustments to optimize the automation process.
Start with an end goal in mind and ask yourself: What problems are you solving for with your automation work? If you can’t answer this question, then you’re not ready to automate any solution.
Reporting
Reporting in the virtual data center details your journey in the virtual data center. It’s a straightforward journey that shouldn’t be overcomplicated. The story will start with details of virtual data center and key performance indicators. It will evolve into a journey of how to get what is needed to expand the delivery capabilities of the virtual data center. With agility, availability, and scalability at the heart of the virtual data center, reporting is the justification for optimization and automation success. Who and what matters as much as having an end goal in mind? The answer is audience and context. Reporting ultimately seeks a decision that needs to be made. No more, no less.
All three of the OAR actions are related. Automation without optimization will lead to work being done without purpose. Optimization and automation without reporting will lead to not getting credit for the work getting done right, and won't inform decision-makers of the proper course of action to take. The better you OAR, the better the data center transformation journey will be for you and your organization.
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