Mastering Cloud Resource Optimization

While cloud utilization grows and the provisioned services become more versatile, new approaches appear to reach both performance and cost efficiency in cloud environments, and cloud resource optimization is one of them.

Regular review and optimization of cloud resources is one of the best practices for reaching the desired balance and is an inevitable part of any FinOps, or Performance Assurance flows. Like other approaches and methodologies, it also evolves and gets enriched by new capabilities and tools.

So, let's look into cloud resource optimization as it currently is, and we are sure you are most likely to find something new about the process further in this article.

What is Cloud Resource Optimization About?

Optimizing cloud resources is strategically configuring and managing them to maximize efficiency and effectiveness. The main goal is to ensure that workloads perform optimally, resources are utilized cost-effectively, and overall cloud performance aligns with business objectives.

It involves numerous tasks that cover different aspects of cloud infrastructure, like selecting the right types and sizes of virtual machines, balancing loads, managing storage, optimizing network configurations, etc. You must also continuously monitor and analyze your cloud architecture to identify and eliminate waste, reduce costs, and improve response times.

Why is Cloud Optimization Important?

Cloud environments are—in most cases—flexible and dynamic. They expand, scale up and down, and incorporate new elements regularly. Meanwhile, cloud vendors offer constant updates and improvements within their services and encourage migration to newer resource types.

On one hand, this gives huge capabilities for improving the performance of cloud resources and cloud-based applications.

On the other, without properly established processes and cloud resource usage monitoring, an enterprise faces the risk of under- or over-provisioning, the appearance of idle resources, ineffective usage patterns, unexpected growth of computing resources, and storage costs.

Meanwhile, without clear visibility into existing resources and their actual usage, planning the updates would be more of a guesswork.

If we look deeper into the question of "Why should we invest in this?" the most important answers would be as follows:

Cost-Effectiveness

One of the main goals of resource optimization is to ensure that all resources within your cloud environment are perfectly balanced between capacity and workload. Finding and migrating to proper resource types, configurations, and purchase options ensures you get the best value for the best price, which is also an important part of cloud cost optimization.

Also, a better understanding of your infrastructure's trends and needs enables your business to invest in the right cloud services and resources, plan the updates properly, and minimize unexpected costs.

Performance and Reliability

Regular resource tracking and analysis, with the planned updates and improvements, ensures that your cloud environment performs at its best. Cloud vendors offer better performance with new resource type generations and services, and when implemented wisely, this brings significant performance improvements in addition to cost efficiency.

The newer resources and the additional attention you pay to the system during the optimization also add to system reliability and uptime.

Enhanced Scalability

Resource optimization tools and strategies enable automated scaling solutions that dynamically adjust computing resources based on real-time demands.

This flexibility makes it easier for businesses to handle unpredictable workload changes smoothly and efficiently without human intervention. It ensures that the infrastructure can grow or shrink in tandem with the needs of the business, providing a scalable solution that supports growth and adapts to varying operational demands.

Security and Compliance

Newer resources often have newer in-built security measures, while regular audits, inventories, and performance analytics make the infrastructure more transparent, enhance monitoring flows, and simplify security scans.

Cloud Resource Optimization Strategies

Resource optimization is a multi-layered process that covers different stages of the infrastructure lifecycle, from efficient planning through ongoing optimization to wise resource elimination flows. During the optimization, the responsible team should consider security, performance, and reliability while considering possible cost-saving opportunities and the business value the planned changes bring.

In general, we can highlight the following most popular strategies that are typically used when building a complex approach to optimization:

However, before introducing changes, you must perform the initial preparation to make the process more effective.

This includes checking the infrastructure for the mismatches between their configuration and actual workload and fixing these mismatches.

This process includes several steps:

1. Performing an inventory of the existing resources, including their configuration (instance type, family, generation, and purpose).

2. Analyzing each instance's performance via indicators such as CPU utilization, memory load, traffic usage, uptime/downtime, etc.

3. Identifying if each specific resource performs normally, is overloaded, or underutilized, based on comparing the configuration against performance indicators.

4. Selecting an optimization approach depending on the results of the analysis.

And now, let us look deeper into the most popular optimization strategies.

Right-Sizing Cloud Resources

Right-sizing verifies that the resource capacity matches its load. This is one of the first steps to eliminate over-provisioning and underutilization within your cloud environments.

For example, you may have a virtual instance with CPU and RAM constantly being over 90% utilized. This means that the resource performs at the maximum of its capacity, and any unexpected peak could lead to performance issues. In this situation, you might want to change the resource type to one with a larger CPU and RAM to give your resource more guaranteed performance and stability.

In the same way, you can detect underutilized resources by finding those with constantly low loads and cutting their configuration to reduce cloud spend without affecting the performance.

Detecting and Eliminating Unused and Idle Resources

When reviewing your infrastructure, you may also detect resources that have a very low load, are turned off most of the time, are not addressed by your applications, or are unattached (for example, storage volumes or IP addresses).

Here is where your further decision may depend on the specifics of each resource. Some of these resources may be a part of "forgotten" infrastructure—created long ago and not used anymore. For these reasons, termination may be the best option.

The other resources are those that are used but rarely. Here, you may have several ways to resolve:

• Merge several resources into one that will cover all their functions.

• Switch from the "on call" usage pattern, when the existing instance is addressed for a specific task and then "waits" for the other one, to "run/delete" one, when you create a pre-configured image and run a new resource when you need the task to be completed, and terminate it, once it is done.

• Consider auto-scaling if a group of idle resources performs the same function.

Migrating to Newer Resource Families and Generations

Cloud service providers regularly expand the library of possible configurations for their virtual resources.

Often, a newer generation has better productivity and is more cost-effective, which provides a double win during the migration.

Also, it is worth considering moving between instance purposes. For example, migrating to a compute-optimized one would add to the performance if the resource usage analytics revealed a constant high CPU load with average or low memory utilization on a general-purpose instance.

Setting Up Autoscaling and Scheduling

Autoscaling tools allow setting up automatic changes in the resource number (horizontal scaling) or configuration (vertical scaling) based on the changing demand in the capacities, thus ensuring that applications perform optimally without over-provisioning or under-provisioning resources.

This not only improves application performance and user experience but also induces cost savings by minimizing the usage of unnecessary resources. Autoscaling also enhances reliability and availability by distributing workloads across multiple instances, reducing the risk of downtime and service interruptions.

Scheduling is another way to manage workloads automatically without affecting their number and capacity. Setting up automatic start/stop allows for significantly reduced costs for resources with regular load and idle intervals.

Considering Different Purchase Options

Another effective cloud cost optimization strategy is to consider available buying options.

Cloud providers typically offer a variety of pricing models and purchasing options, such as on-demand instances, reserved, and spot ones, each with its own pricing structure and usage commitments.

Having analyzed your cloud environment for resource utilization and load, you can use these data in long-term capacity planning and build a proper balance between:

• On-demand instances provide maximum flexibility as they allow you to pay as you go without any long-term commitments. This is ideal for unpredictable workloads that cannot be interrupted.

• Reserved instances enable you to commit to a specified compute capacity for a fixed period (typically 1-3 years) at a significantly reduced rate compared to on-demand pricing. This option suits predictable workloads with stable, long-term requirements and can greatly reduce costs.

• Spot instances offer the opportunity to purchase unused compute capacity at heavily discounted rates compared to on-demand prices. However, the provider can interrupt these instances with a brief notice when the capacity is needed elsewhere. This benefits flexible, non-critical workloads such as batch processing or background tasks.

Different purchase options can help you maximize savings, ensuring that this does not affect the performance and minimizes possible resource waste.

Containerization

Containerization is one of the efficient ways to optimize cloud computing cost, performance, and resource utilization.

You can make multiple applications to share the same operating system kernel and, when possible, binaries and libraries, making them very lightweight compared to virtual machines (VMs). This efficient use of system resources (CPU, memory, storage) means more applications can run on the same capacity, leading to better utilization of underlying hardware.

Also, containers offer consistency across various environments and cloud platforms. This consistency reduces the need for reconfigurations and troubleshooting, which can consume substantial cloud resources, developer time, and unnecessary costs.

Building Multi-Cloud Environment

Using multiple clouds allows you to use the strength of different cloud providers by selecting the best-matching cloud services and pricing models and establishing effective resource allocation based on the specific expectations—from your side and offering details—from the cloud provider's.

This approach prevents vendor lock-in, giving organizations the flexibility to shift and adapt resources responsively to changes in pricing, service quality, demands, and integration of new technologies. It also improves geographical reach and compliance, facilitates disaster recovery, and supports business continuity by spreading resources across multiple clouds.

Using AI for Resource and Cost Optimization

With AI technologies boosted, many cloud optimization services and tools gained new capabilities, and new offerings and approaches arose.

It is not a specific strategy but a powerful means to improve a wide set of processes, bringing them to a new efficiency level. Here are some specific ways AI can be used:

• Predictive analytics: AI can analyze patterns and trends from historical data to forecast future usage and demands. This enables proactive resource allocation, ensuring the right amount of resources is available at the right time, thus avoiding underutilization or overprovisioning.

• Automated scaling: AI algorithms can help implement effective autoscaling processes, improving resource allocation to an application based on real-time usage data.

• Load balancing: AI can intelligently distribute workloads across the most efficient compute resources, improving response times and application performance. This also helps maintain system stability and avoid blockages.

• Cost management: AI can improve the recommendations of the most cost-effective cloud configurations and purchasing options (like spot instances or reserved instances) depending on the workload requirements and usage patterns and help monitor cost anomalies.

• Anomaly detection and resolution: AI can monitor cloud environments for unusual activities, suggest performance issues or security threats, and suggest or initiate corrective actions.

Thus, AI provides the tools and capabilities to automate and refine cloud resource management, making it smarter, more responsive, and more cost-effective.

Tools for Optimizing Cloud Resources

There are many cloud optimization tools, both offered by cloud providers themselves and third-party vendors. They can be focused on general optimization or dedicated to a specific area—optimizing costs, resources, security, etc.

When selecting the one to be used for your enterprise, it is important to consider functionality and things like support of different cloud providers, integration with other tools, ease of use, and pricing.

For instance, EPAM Syndicate RightSizer (SRS) is a great example of a tool focused on effective resource capacity planning and management.

It supports a unified approach for single and hybrid cloud resource optimization, allowing you to check your virtual machines in AWS, Microsoft Azure, and Google Cloud Platform. It analyzes each resource configuration, load, and usage patterns. It builds AI-empowered suggestions on how (and if) the configuration or lifecycle should be changed to reach the best match between the resource capacity and utilization. This allows for the effective elimination of overspending with underutilized resources and the detection and fixing of cases when the current capacity is not enough to face the expected load properly.

Built based on the API-first approach, SRS can be easily integrated with other tools.

The comprehensive information it provides is enough to build any analytic reports that can list overutilized, underutilized, or unused resources and detailed rightsizing recommendations, including scaling, shutdown, and scheduling—all accompanied by the expected cost savings.

The engine behind the tool was trained and tested on a vast amount of cloud usage data within various infrastructures with the assistance of highly skilled Cloud experts. The performance analysis showed that most recommendations fit customers' use cases. The reinforcement learning mechanism allows you to adjust SRS's recommendations to your case.