What is Cloud Optimization & Why is It Important?

Cloud Optimization is About Delivering Business Efficiency

Cloud optimization is the process of correctly selecting and assigning the right resources to a workload or application. When workload performance, compliance, and cost are correctly and continually balanced against the best-fit infrastructure in real time, efficiency is achieved.

Every application’s and workload’s needs for infrastructure are unique—and these requirements evolve over time. Baseline performance is traditionally achieved by applying domain knowledge when selecting resources for a workload, but all workloads that have been manually matched to cloud resources can benefit and be further optimized using machine intelligence.

Apps that have their ongoing needs dynamically matched to the optimal cloud supply run better, require fewer resources to manage and less supporting infrastructure on-premises and in the cloud, and deliver maximum value for cloud dollars spent.

Why Organizations are Concerned about Cloud Optimization

Cloud optimization is a deliverable for IT Operations teams—specifically CloudOps or DevOps, depending on the organization—who are responsible for resource allocation.

These highly-skilled individuals are often caught between requirements leveled by Finance departments—who want to control cloud spend, and application owners, who never want to hear that the resources for their apps are being reduced.

For modern organizations to be successful, it is critical that IT Ops have control over the reign of these stakeholders. Cloud optimization makes this possible, allowing CloudOps to maximize cloud value-for-spend (soothing Finance Analysts and the CFO) and deliver topmost app performance and reporting thereof (making application owners and their end users happy).

Buying services that don’t match application requirements creates risk and unnecessary cloud spend—results businesses cannot tolerate.

Use Cases for Public Cloud Optimization

  • Application risk reduction by rightsizing instances based on intra-day workload pattern
  • Infrastructure reduction and modernization through cloud instance rightsizing
  • Container optimization
  • Better matching through cloud provider comparison
  • Automation through self-aware instances (application-aware cloud)
  • Automation through self-optimizing cloud apps
  • Cost reduction and compliance through public cloud billing visibility

Hybrid Cloud Optimization Use Cases

  • Proactive optimization and realtime response for virtual infrastructure and bare metal cloud
  • Workload routing and reservation
  • Elastic cloud for VMware infrastructure
  • Digital transformation through efficient cloud migration planning and modeling
  • Software license optimization
  • Storage resource optimization
  • Server refresh and transformation analysis

How Cloud Optimization is Achieved

Workload Modeling, Service Matching, & Optimization Recommendations

Cloud optimization solutions model and analyze the patterns of each workload—including usage, history, and operational cost, combine this analysis with knowledge of the cloud services and configurations available, then deliver recommendations to improve workload-to-service matching. These insights help IT Operations make better cloud decisions and accelerate business innovation.

Cloud Cost Visibility & Optimization

Cloud costs and complexity expand exponentially as infrastructure grows. Although first-generation cloud optimization solutions focus on cloud cost visibility and spend optimization, these are an inherent byproduct of more sophisticated next-generation solutions that are built primarily to ensure broader efficiencies like app performance and risk reduction for organizations.

Workload Automation & Continuous Optimization

Newer-generation solutions go beyond making recommendations to actually allow organizations to automate the implementation of these suggestions to whatever extent they are comfortable. This workload automation can be partially achieved by using the optimization recommendations to kick off workflows in IT service management platforms like ServiceNow, or even made ongoing through optimization-as-code-driven continuous optimization.

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