Intel Cloud Optimizer
HCLTech uses Intel Cloud Optimizer to find cost-saving measures and make better use of cloud resources.
Difficulty
Understanding operational requirements and having visibility into resource utilization are critical in cloud management. These components proved to be difficult for HCLTech. The business faced the challenge of having little visibility into how its vast infrastructure was using its cloud resources. This, together with changing operating requirements, made manual cost optimization a difficult and time-consuming effort.
Resolution
A software as a service (SaaS) called Intel Cloud Optimizer was made available by HCLTech. Its purpose is to provide data-driven insights and suggestions for optimizing cloud instances. By utilizing cutting-edge algorithms and analytics, the Intel Cloud Optimizer gives HCLTech a solid scientific foundation on which to base its decisions about cloud infrastructure and, consequently, attain cost-effectiveness for its cloud workloads.
Outcome
Through this partnership with Intel, HCLTech was able to pinpoint 34% of possible cost savings and 30% of immediate cost savings prospects without sacrificing performance.
The Requirement for Cloud Computing Cost-Efficiency and Optimal Resource Utilization
One of the main issues HCLTech has when utilizing cloud infrastructure is finding the ideal balance between cost, uptime, and performance: what to buy (family), how much to buy (size), and how to guarantee the features that are best suited to enable particular workloads to operate and scale in the cloud at a reasonable cost (features). Finding and putting into practice the best characteristics that let particular workloads run smoothly and grow economically in the cloud environment are essential to striking this balance.
In order to achieve performance needs while minimizing costs, this involves negotiating difficulties including choosing the right cloud instance types, establishing resource allocations, and optimizing workload distribution. The workforce cannot adequately determine these crucial aspects by manual or estimate methods.
Cloud instances from several cloud service providers (CSPs), including AWS, Microsoft Azure, and Google Cloud Platform (GCP), are used by HCLTech. These well-known CSPs offer free onboarding and optimization services. These studies and services, however, frequently have drawbacks. Purchase plans and billing optimizations are their main areas of concentration. They also provide generic statistics, lack granular controls, offer tooling that is not customized for each user, and don’t explain the reasoning behind their conclusions. HCLTech need analytics that were rigorous enough to produce precise results and remove uncertainty so they could choose the best cloud instances for particular workloads, which can save a substantial amount of money.
Using Intel Cloud Optimizer to Select the Best Cost-Saving Option
To assist in recommending the optimum cloud instances that can result in cost savings, HCLTech resorted to the Intel Cloud Optimizer. HCLTech now has a complete solution for evaluating resource utilization in multi-cloud scenarios, including AWS, Azure, and GCP, thanks to the Intel Cloud Optimizer. It finds unused resources and provides customized cost-saving solutions that meet HCLTech’s unique needs by employing historical data and cutting-edge machine learning techniques.
In addition to helping HCLTech connect to the best resources across a multi-cloud environment, Intel Cloud Optimizer offers analytics capabilities and machine learning (ML)-informed recommendations. It also gives HCLTech a scientific basis for cloud instance recommendations based on a wide variety of critical inputs, such as workload history, business unit (BU) policies, and technical constraints. Based on price, stability, performance, or other factors particular to HCLTech’s requirements, it provides insightful recommendations.
The Intel Cloud Optimizer’s capability to automatically gather data via standardized APIs from different cloud settings is one of its primary benefits. This guarantees a private and safe method of collecting data, enabling HCLTech to get precise insights without sacrificing security. Moreover, policy-driven optimization is made possible by the platform, giving HCLTech the ability to modify and enhance the analysis in accordance with their own operational demands. This involves having the freedom to adjust variables like bursting family kinds, limiting the size of resources that can be used, and concentrating optimization efforts on particular cloud environments.
The Mechanism Underpinning the Optimization
To enable Intel Cloud Optimizer to ingest up to two months of workload history maintained by the CSP for all recognized cloud instances, HCLTech only needed to provide their CSP cloud credentials. Additionally, the optimizer keeps and retains up to 90 days of workload information for a continual optimization process, which allows it to improve and refine its recommendations over time. The optimizer uses additional technical or business limitations and flags in addition to workload history to analyze CSP instance utilization and make sure the results are actually “fit for purpose” and actionable.
The optimizer creates a profile for each job across compute instances using proprietary analytics models, then suggests the best kind of instance for each workload based on its learnings from resource usage patterns. Because they cover all necessary factors and specific, detailed workload behaviors, these recommendations are actionable. HCLTech can proceed with confidence, switching to the suggested instance type to help achieve their objectives for each workload, including cost effectiveness, uptime, and/or performance thresholds.
To assist provide a complete picture of how the optimizer draws conclusions, each recommendation given by the optimizer is accompanied by comprehensive detail on what went into the analysis policy, workload history, and technological restrictions. This evidence gives HCLTech the assurance it needs to implement the change and enjoy the rewards.
Creating Gradual Gains with All-Inclusive Cloud Optimization
HCLTech has reaped numerous benefits from the Intel Cloud Optimizer, including increased cost-effectiveness and efficiency in its cloud operations. Above all, the optimizer successfully decreased the total amount spent on cloud infrastructure, which contributed to large cost savings. This reduction helps to increase profitability in addition to freeing up funds for other important projects.
Furthermore, in HCLTech’s cloud environment, resource efficiency has improved significantly thanks in large part to the Intel Cloud Optimizer. The optimizer helps minimize problems associated with under- and overprovisioning by facilitating the effective distribution of resources, ultimately maximizing the use of available cloud resources.
Offering data-driven insights is one of the optimizer’s other main benefits. The platform provides HCLTech with important insights through a thorough study of resource usage, enabling well-informed and strategically sound decision-making processes. The organization can make educated decisions about its cloud infrastructure thanks to these accurate and useful insights.
Faster optimization of cloud resources is another benefit of the Intel Cloud Optimizer. The platform helps HCLTech to quickly adopt cost-saving strategies by producing simple-to-follow recommendations, which leads to immediate and noticeable advantages.
Getting Results That Matter
With the aid of the Intel Cloud Optimizer, cost reductions were realized more quickly, enabling HCLTech to get the most out of its cloud investments. These investments include:
- Possible financial savings. From its $11 million cloud investment, the solution found opportunities for HCLTech to possibly save over $3.7 million annually, or a significant 34 percent reduction in costs.
- Quick opportunity to save money. The approach, which provided a noteworthy 30% cost reduction, found over $60,000 in monthly cost-saving options without sacrificing performance through careful study.
- Reduction of risk. In addition to saving money, the solution takes care of resource allocation issues by proactively identifying them. This allowed HCLTech to make the necessary corrections, which improved system performance and stability by as much as 6%.
- Optimization based on policies. HCLTech may now customize optimization rules based on operational needs thanks to this solution. This adaptability guarantees that suggestions are in line with HCLTech’s procedures and standards, improving precision and optimizing possible cost reductions.
- No-risk implementation. The solution simply connects with current infrastructure without the need for extra agents by using conventional APIs for data collection. This non-intrusive method prevents interruptions and allows for seamless setup and operation.
In summary
The way in which HCLTech has integrated and employed the Intel Cloud Optimizer is a testament to the revolutionary nature of data-driven cloud optimization techniques. HCLTech has the ability to not only achieve significant cost reductions but also transform its approach to resource management in the cloud by utilizing cutting-edge machine learning algorithms and the insights obtained from thorough resource utilization research.
