Optimizing for Price and Performance in the Cloud

Optimizing for Price and Performance in the Cloud | CIO Dilok Klaisataporn

In their rush to the cloud, companies can easily end up with significant waste by taking a “best efforts” approach to aligning cloud instance types and sizes to workloads.

Businesses, particularly those that are relatively new to the cloud, often overprovision resources to ensure performance or avoid running out of capacity. The result is that their workloads may consume a fraction of the resources being paid for. Even organizations experienced with cloud infrastructure can waste 20% to 30% of their cloud spending on capacity that simply isn’t needed.

Compounding the challenge is the fact that the major cloud service providers (CSPs) offer as many as 600 different service options based on factors such as processor type, memory configuration, storage, networking, hypervisor, and other variables. Understanding all these options is impractical – if not impossible – for humans, let alone determine the best fit for a given workload, especially at scale. What’s more, the cloud options, and workloads being hosted, change all the time.

Complexity is amplified by the fact that 90% of enterprises use multiple clouds, according to IDC.[1] Relying on people to manually select the right cloud instances is a risky proposition as even small mistakes can add up to big unanticipated costs. Analytics that take the guesswork out by determining the best selections, and ultimately automating instance configuration, is key. IDC research shows that

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