5 VM Routing Mistakes Made in Private Clouds

    Cloud management platforms (CMPs) are being implemented by enterprises now more than ever to manage and automate the flow of new workloads into the enterprise. However, no commercially available CMP today takes a deep enough look at the workload’s resource, technical and business-related requirements to make these routing decisions. Most rely on logic that sends workloads into environments using simplistic tags that match workloads to environments and lack the ability to truly balance demand and supply for the best use of available resources. Making the right decision on where to place a workload is crucial because the wrong decision can result in performance issues, the cost of re-work and re-deployment, and inefficient use of available infrastructure.

    In this slideshow, Cirba CTO and co-founder Andrew Hillier shares five ways where not having the proper decision logic in place to make the right routing decisions can cost your organization.

    5 VM Routing Mistakes Made in Private Clouds - slide 1

    Costly VM Routing Decisions

    Click through for five ways not having the proper decision logic in place to make the right routing decisions can cost your organization, as identified by Cirba CTO and co-founder Andrew Hillier.

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    Not Balancing Demand Across Environments

    Correctly balancing workload demand is very complex. Frequent poor routing choices also lead to prematurely closing-down an environment so that no new workloads can be placed in it. Routing can cause this problem by placing too many workloads into an environment that are heavy consumers of one resource type, saturating the environment with demand for memory, for example. By making routing decisions that consider the demands associated with the new workload, the existing workloads in an environment and its available resources, demand can be better balanced across environments to ensure greater utilization overall. 

    5 VM Routing Mistakes Made in Private Clouds - slide 3

    Over-Licensing Software

    Certain applications offer software licensing models for virtual infrastructure on a per-processor or per-core basis. In effect, this permits the licensing of an entire host, which enables an organization to run an unlimited amount of VMs requiring that certain application (such as Windows OS) on that host. This creates a significant opportunity, and by concentrating workloads requiring certain license types to certain environments and certain hosts, it can significantly reduce costs. Conversely, operating or planning environments without considering this factor can increase licensing costs significantly.

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    Over/Under Servicing Workloads

    Poor workload routing decisions will lead to resource shortages and associated performance issues. In order to avoid this situation, most organizations err on the side of over-servicing workloads. This inflates costs and isn’t actually required with the right control system in place. Analytics can help organizations establish and maintain fit-for-purpose infrastructure to ensure workloads get access to the resources they need without over-servicing them.

    For example, when booking a hotel, few people would book a penthouse if all they need is a regular room (unless somebody else is paying for it). Similarly, not every workload requires access to gold tier storage, or 99.999 percent availability. But many will take it if they have no way of knowing what they truly need (or if somebody else is paying for it). The better approach is to scientifically analyze app requirements against environment capabilities to find a home for each application that gives it access to just the right type of resource.

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    Non-Compliance of Regulatory Policies

    Non-compliance of regulatory policies can have a wide range of associated costs, often resulting in financial penalties (such as up to $500,000 for not being PCI compliant), to legal actions, and even suspension of business operations. Although some of these constraints are fairly easy to deal with, others can be quite complex and require more sophisticated policies. For example, certain users’ applications cannot reside on the same infrastructure (e.g., traders vs. researchers), which means that where it goes is a more complex function of what it needs and what is already running in the target environments.

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    Last but not least, placing workloads into the wrong environment can often result in costly “rework” to make things right. This is not a simple matter of stopping VMs and starting them somewhere else – rework also incurs costs due to delayed access to the workload that was to be deployed, and requires significant manual effort to roll back and re-do the change management and service delivery processes. If the workload actually processed production transactions, then data snapshotting and migration may be needed, and end users will experience what is now a service interruption, not just a delay in initial access.

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