Cloud migration has passed the tipping point, with nearly 60% of North American enterprises now relying on public cloud platforms, five times the percentage that did just five years ago. According to research by IBM’s Institute for Business Value, 98 percent of surveyed organizations plan to use multiple hybrid clouds by 2021, another sign of tremendous cloud momentum.

Despite this trend, migration to the cloud and the transformation to a hybrid multi-cloud IT operating environment is often viewed with anticipation and anxiety, and perceived as complicated. Most companies require an approach that ensures that their data migration is smooth and fast, putting pressure on the IT team managing the process. No matter how much data you want to move, there are many ways (some of them quite surprising) to actually transfer it to your preferred cloud service provide (CSP). As AWS notes, one should never underestimate the bandwidth of a semi truck filled with disks hurtling down the highway.

Leading CSPs AWS, Azure, Google and IBM each provide data migration services, and they assume that you have already decided to move to the cloud, you’re ready to transfer your data, and you have chosen your CSP. Each of them has carefully refined the process of getting your data from your on-prem servers to their cloud servers. After all, successful migration is a critical component of their customer acquisition strategies.


However, before you sign up with any service provider to migrate to the cloud and all the features that come with it — like a multi-tier read-write cache, deduplication, pre-fetching, asynchronous write-back, and network optimizations — you must first conduct a thorough independent assessment of your existing on-prem workloads. This will save the IT operations team significant time and provide accurate cost projections.

To begin, each of your workloads must be carefully matched to an appropriate cloud instance. A pre-migration workload assessment should automatically analyze a CSP’s general purpose, memory-optimized, and CPU-optimized instance types and identify which is the most cost-effective for each of your workloads. Identifying the right instances for a single workload from a single CSP can be like navigating a maze. Check out this comparison of Amazon EC2 instances and you’ll see what I mean.


In addition, your actual CPU and memory requirements must be analyzed by workload to determine baseline and burst characteristics for mapping to the appropriate static or on-demand instances. This is where a cloud migration cost assessment that analyzes down to the workload really shines because the cost differential between static and on-demand instances can be quite significant. Static or “reserved” instances are more expensive because the associated memory and CPU resources in the cloud are dedicated. These are ideal for applications that have steady state or predictable usage.

On-demand instances make a best effort to provide resources when needed. Applications with short-term, spiky, or unpredictable workloads that cannot be interrupted are a good match for on-demand instances which increase or decrease your compute capacity depending on the demands of the application. The cost benefit is that you only pay for what you use. So you can see now that understanding your individual workload profiles and mapping those to the optimal cloud instances can result in measurable economic benefits.


It would be remiss of me to not mention the application performance benefits that detailed workload mapping provides. When conducting a cloud migration cost assessment, it’s essential to calculate the cloud CPU and memory needed to achieve the same performance and SLAs that are delivered by your existing on-prem infrastructure. Missing this critical step can result in application performance failure and loss of revenue, depending on the service the application supports.

CSPs generally make it easy for you to map your on-prem VM to a cloud VM, but fail to adequately profile and analyze each of your workloads before you commit to their services. Without that detailed analysis, it’s impossible to select the best instance options for each workload and accurately calculate cloud costs. That’s where the free Pepperdata Cloud Migration Cost Assessment comes into play.


Pepperdata can automatically analyze and profile every workload in your cluster to accurately determine your projected cloud costs, and provide the most appropriate instance recommendations for workloads, queues, jobs, and users…before you commit to a cloud service provider. We also map your big data workloads to various instance types to meet SLA requirements and enable you to compare services and costs across AWS, Azure, Google Cloud, and IBM Cloud.

Use our free Big Data Cloud Migration Assessment to get a detailed understanding of your on-prem workloads, with recommended instance options and cloud cost projections before you commit. You’ll be equipped to make better-informed, fact-based cloud migration decisions based on the most comprehensive workload performance profiles available – saving you significant time and expense.

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