Launching a successful cloud migration strategy that doesn’t result in performance degradation or unsatisfied users requires analyzing both a company’s own infrastructure as well as the numerous public cloud options available. Unfortunately, many cloud migration strategies still rely on extremely time-consuming manual analysis.
Taking a manual approach to analyzing data and planning a cloud migration strategy is not only incredibly time-consuming and tedious, but it is also highly prone to error. It can cause applications to be missed, resources to be over- or under-provisioned, and it can ultimately lead to failed migrations. Additionally, many of the steps you need to take to build a strategy are nearly impossible if you rely solely on manual methods.
To build a successful cloud migration, here are 6 important analyses that must be completed using automated analytics:
Convert On-Premises Metrics to the Cloud Using Benchmarks: Benchmarking is an important part of the cloud migration planning process, because it allows you to predict how workloads will perform in the cloud before executing a migration. Benchmarks translate on-premises performance metrics to the cloud. Without benchmarks, it’s impossible to calculate an accurate cloud TCO, to determine which applications to move and when, or to migrate to a right-sized environment.
But unlike on-premises environments—which have comprehensive, publicly available benchmarks—there are no publicly available benchmarks on CPU and memory for instances in the public cloud. The lack of publicly available benchmarks means that companies are on their own to convert on-premises metrics to the compute and memory capabilities of each machine in the cloud. Attempting to accurately determine this manually is very, very challenging.
One major IT consulting company learned this lesson the hard way. After spending 6 months attempting to manually analyze over 1,600 machines and 500 applications to see how they would map to AWS, it realized that it needed a faster, more accurate way to convert on-premises metrics to identify the right-sized compute options for each workload the company was migrating. This company used automated analytics and performance benchmarking in the Cloudamize platform and was able to both expedite the process of converting its on-premises metrics to the cloud while also taking advantage of Cloudamize’s library of benchmarks for the myriad options in AWS. As a result, it was able to move to right-sized compute resources immediately upon migration—saving $4 million in cloud costs.
Identify Right-Sized Storage Options: The most complex element of cloud migration strategy planning is storage. There are various storage options across the three major public clouds, including credit-based GP-SSD storage from AWS and hard or fixed performance-based premium storage P4-P50 from Azure. Each of the many storage options works differently and has different performance limitations. With so many different options designed for so many different purposes, most businesses are at risk of making a wrong decision, which leads to unhappy users, extensive debugging, and failed migrations.
To understand how each storage option will perform for different workloads (and the right combination of compute and storage for each workload), you have to capture analytics, convert them to the cloud, and compare them to all of the cloud storage options. Attempting to undergo this process manually is impossible, which is why automated analytics are essential to selecting storage options.
The data needed to determine the right storage options include:
- On-premises storage performance metrics
- Required peak IOPS
- Available maximum IOPS
- Disc Capacity
- Disc Occupancy
- Required Peak Throughput
- Operation Sizes
Automated storage benchmarking enables on-premises storage metrics to be accurately converted to cloud storage metrics. This allows the best cloud storage options to be identified for each workload and to predict the performance and cost of the recommended storage options. With automated storage analysis, the best combination of storage and compute resources are identified to ensure cost-performance optimization upon migration.
Conduct Discovery and Dependency Mapping: Understanding exactly what exists in an on-premises environment is one of the biggest challenges of the cloud journey. Every step of the migration planning process, including calculating TCO, predicting cloud performance, mapping dependencies, and designing the migration plan, can only be successful if it’s based on accurate discovery.
Unfortunately, configuration management database (CMDB) assessment, which is a common manual discovery method, is inadequate because it relies on out-of-date databases and causes organizations to overlook many elements of their environment that may not be contained within the CMDB. Spreadsheet analysis based on a manual assessment of applications is also likely to be unreliable and miss shadow IT.
The only way to get an accurate analysis and precise understanding of an IT environment is with automated data analysis. Automated analytics can track all commands and web server requests at high frequencies, so every application, including Shadow IT and those that have bursty or short-lived connections, can be identified - along with their dependencies at a deep level.
Conduct Performance Analysis: The success or failure of a cloud migration strategy rests heavily on a company’s ability to accurately analyze on-premises performance. Unfortunately, many manual methods of analyzing performance do not provide an accurate picture of what performance looks like. They often rely on “averaging” methods that fail to account for bursty behavior and peaks and valleys.
Automated performance analysis collects performance metrics very frequently over a period of at least two weeks to offer a detailed performance analysis of compute, storage, and network resources. This data provides a complete performance portrait of infrastructure with all of the peaks and valleys in usage, so companies can accurately provision their resources. This is essential for identifying the best-possible compute and storage options in the cloud for each workload.
Keep up with Cloud Changes: At any given time, the three major public clouds offer millions of possible configurations for your workloads. The compute and storage options and their pricing change frequently, and staying on top of this is challenging. Automation can keep up with these changes efficiently, so you can be assured that you’re always recommending the best right-sized options.
Use Automated Analysis to Break Down the Complexity of the Cloud
The enormous complexity of the cloud, as well as IT environments, mean that manually planned migrations are time-consuming and prone to error. In fact, organizations that don’t have automated analysis can spend as much as 48% more than they need to spend due to waste and over-provisioning. Fortunately, automated analytics tools can reduce this complexity so that cloud experts can more quickly plan accurate cloud migration strategies for their clients.