August 14, 2023 By Spencer Mehm 4 min read

Organizations worldwide are embracing the power of cloud computing to drive innovation, enhance scalability and improve operational efficiency. Among the various cloud service providers available, Amazon Web Services (AWS) has emerged as a popular choice for businesses seeking digital transformation. The flexibility, scalability and breadth of services offered by AWS have enticed organizations to migrate their workloads to the cloud giant. However, while the benefits of such a migration are substantial, there are critical considerations that must not be overlooked. 

Migrating workloads to AWS requires careful planning and execution to ensure a seamless transition. Failure to do so can result in unforeseen costs and performance issues that can lead to downtime, poor end-user experiences and blown IT budgets.  

In this blog post, we will discuss the significance of efficiently migrating workloads to AWS and explore how organizations can better navigate this complex process with IBM Turbonomic

Life and shift vs. optimized migration planning

When migrating to AWS from your on-premises environment, organizations must first decide what migration strategy they would like to employ.  There are “lift and shift” (i.e., re-hosting) and “optimized” modes of migration. Both methodologies will lead you to the cloud, but they differ in the resulting application performance and cost. Let’s begin by examining these two popular cloud migration strategies.  

A lift-and-shift migration is the process of matching current on-premises virtual machine (VM) instances and storage tiers to their closest equivalents in the cloud. Sometimes this method serves an organization in a transitionary state before it employs a more cloud-native strategy.  

Lift-and-shift migrations are typically faster but can be more expensive and may pose risks to performance. Often, the main force behind this strategy is financial pressure, which leads to time constraints. Ideally, organizations have the time, money and resources to evaluate and rearchitect each application workload before migrating. In reality, this scenario is usually not the case, and organizations that migrate quickly pay the price somewhere down the line. 

Optimized migration strategies, on the other hand, examine VM and storage historical utilization metrics to select the best VM/instance type and storage tier in the selected cloud provider’s region. This method addresses many of the concerns that come with using a lift-and-shift migration. When migrating workloads at their optimized-size, companies can quickly adapt to the elasticity and scalability of the cloud to ensure workload performance at the lowest cost.

How IBM Turbonomic optimizes AWS cloud migrations

The IBM Turbonomic platform differs from other cloud migration tools in that it delivers potential application migration plans that detail specific actions and indicate which cloud configurations will support your workloads if you take a lift-and-shift versus an optimized approach. IBM Turbonomic generates these plans by analyzing the real-time resource needs of application workloads, whether they’re cloud-based or running on-prem. This migration assessment strategy helps organizations evaluate potential benefits and drawbacks of a lift-and-shift versus an optimized cloud migration strategy and enables organizations to achieve cost savings by discouraging expensive lift-and-shift migrations.  

Diagram A shows how Turbonomic presents this side-by-side comparison of the two possible plans: 

Diagram A

As shown in Diagram B, Turbonomic also provides a side-by-side comparison between a lift and shift and optimized migration plan that customers can review down to each individual action. In this case, for virtual machine mapping: 

Diagram B 

Customers can also view the specific action details for the actions that make up each plan. In Diagram C, you can see the rationale behind moving a virtual machine as part of an optimized migration to AWS: 

Diagram C

It is important that new AWS users take advantage of the discounted pricing that AWS offers. As part of Turbonomic software’s cloud migration planning capabilities, it examines billing and price adjustments negotiated with AWS and creates migration plans that account for discounted pricing. These cloud migration plans include moving workloads from on-demand to discounted pricing through additional purchases of reserved instances. 

In Diagram D, you can see the specific RI buy actions that Turbonomic recommends: 

Diagram D 

Optimize cloud consumption from the start 

Regardless of what migration strategy your organization chooses to employ when migrating to AWS, the step-by-step planning capabilities of IBM Turbonomic allow you to easily weigh the benefits of the different strategies and ultimately helps ensure that, once you migrate to the cloud, you are proactively reducing the amount your workloads consume and the price your organization pays. Learn more about how Turbonomic can optimize your AWS migration here or start your free trial below. 

Start your free trial
Was this article helpful?
YesNo

More from Automation

Making HTTPS redirects easy with IBM NS1 Connect

3 min read - HTTPS is now the standard for application and website traffic on the internet. Over 85% of websites now use HTTPS by default—it’s to the point where a standard HTTP request now seems suspicious.  This is great for the security of the internet, but it’s a huge pain for the website and application teams that are managing HTTPS records. It was easy to move HTTP records around with a simple URL redirect. HTTPS redirects, on the other hand, require changing the URL…

5 SLA metrics you should be monitoring

7 min read - In business and beyond, communication is king. Successful service level agreements (SLAs) operate on this principle, laying the foundation for successful provider-customer relationships. A service level agreement (SLA) is a key component of technology vendor contracts that describes the terms of service between a service provider and a customer. SLAs describe the level of performance to be expected, how performance will be measured and repercussions if levels are not met. SLAs make sure that all stakeholders understand the service agreement…

Scale enterprise gen AI for code generation with IBM Granite code models, available as NVIDIA NIM inference microservices

3 min read - Many enterprises today are moving from generative AI (gen AI) experimentation to production, deployment and scaling. Code generation and modernization are now among the top enterprise use cases that offer a clear path to value creation, cost reduction and return on investment (ROI). IBM® Granite™ is a family of enterprise-grade models developed by IBM Research® with rigorous data governance and regulatory compliance. Granite currently supports multilingual language and code modalities. And as of the NVIDIA AI Summit in Taiwan this…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters