5 Common Issues Hindering Cloud Instancing Optimization

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Public cloud environments like AWS, Azure, Google, and Microsoft are known for being cost-effective for IT infrastructure. Sadly, this doesn’t happen each time because of the constantly increasing complexity in what the cloud offers to it’s users. Due to lack of knowledge and visibility about the cloud environments, most organizations fail to deal with the cost and unknowingly overfill money into public clouds. This is one kind of an error that makes it very difficult to be controlled or fixed.

Here are five mistakes that you should definitely avoid to increase the cloud instancing optimization and resource efficiency without affecting the performance risks in new environments.

1. Not having Command on Detailed Application Workload Patterns

It is not necessary that all workloads are designed irrespective of which public cloud you’ve been hosted with. You meet the error when you go deeper into cloud instance selection. For selecting the cloud instance, you have to be well informed about the reason for the workload, along with the utilization pattern.

The system of a batch of workload is different than the usual application. In apps, constant varying from peaks to valleys occur. While in public cloud, the workload does its work at least once at the end of each day. In order to understand the workload pattern between the days and its variation over a business cycle, you wisely have to select the right cloud instance and resources.

2. Inability to Meet Benchmarks to Normalize Inter-Platform Data

‘Like for Like’ is a common approach to deal with resource allocation when migrating from one cloud environment to another. There is a dire need to utilize benchmarks in order to standardize the workload information. It gives a more clear idea of how the workload will perform in the new environment.  This way you can expect more optimized performance on the new platform, which is likely to have better hardware than the previous one.

In simple words, we can say that normalizing the data is the only way to make an apple to apple comparison.

3. Focusing too much on the Sizing

Now, while sizing is an important issue to consider, it shouldn’t be the only focus. When you migrate to an environment that is running on more advanced and powerful hardware, you can reduce cost by modernizing workload as well. Compared to the savings delivered by right sized instances on the public cloud, modernizing can add 20% more value.

Modernizing requires proper understanding of the workload, catalogs, and cost, along with the ability to normalize the data. It all adds up to performance optimization. The analysis of all of these aspects should be an ongoing process, especially for apps that are regularly updated.

4. The Bump-Up Loop

One of the major culprits behind overspending or over-provisioning is the infamous bump-up loop. Certain workloads will use as many resources as they are provided. When more resources are provisioned, those apps will take more of it and they would do it for short cycles. This is the bump-up loop and it can literally hog resources and multiply the spending.

In order to prevent this expensive situation, you need to analyze and understand the behavior and pattern of each workload. This way the overall resource allocation and utilization will be optimized. 

5. Unmanaged Zombie Cloud Instances

Zombie instances occur when the short-term instance is deployed but not shut down. A single zombie instance may not cause much harm, but a pile of them can send the cost skyrocketing. A reasonable approach to avoid such waste is to keep an eye on the workload pattern across a certain time period i.e., a monthly or weekly cycle. Tools that are unable to provide long-term visibility into the workload are usually incapable of identifying zombie instances.

Conclusion

Avoiding all of these mistakes can optimize your cloud instancing, not just for cost but also for performance.

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