putilov_denis - stock.adobe.com
5 ways AI can boost cloud performance optimization efforts
AI-powered tools can improve cloud environments by predicting issues, optimizing resources and automating scaling, enabling administrators to enhance performance and cut expenses.
Extensive single-vendor deployments, tightly integrated hybrid environments and complex multi-cloud constructs are commonplace among businesses. These multifaceted deployments highlight the importance of AI-based tools for cloud performance optimization and cost reduction. On average, CIOs reported that their actual infrastructure and application cloud spend is 30% over what they anticipated, according to Azul's "The CIO Cloud Trends Survey & Report."
Continued and extensive growth in the cloud and AI industries creates numerous opportunities for advancement. However, this also puts pressure on cloud admins and IT teams to stay informed on new AI capabilities and technologies in a rapidly changing market. While human oversight of AI is an imperative, businesses must also embrace adoption to ensure they remain agile and relevant by quickly integrating AI and its analyses into their cloud optimization strategies.
Today's cloud leaders can use AI for cloud performance optimization and ensure reliability. Learn the ways AI can reduce manual workload, provide smarter automation and increase cloud cost savings, creating a better overall environment.
1. Proactive issue mitigation
AI offers predictive analysis of existing and potential problems based on real-time metrics and historical data. Self-tuning capabilities within AI enable it to meet traffic and workload demands based on projected forecasting. This enables IT teams to mitigate issues before they result in additional cloud costs or performance degradation. Admins can even enable AI to trigger automated workflows for common performance issues.
Offerings, like Splunk Observability Cloud, ensure admins can be proactive when mitigating potential issues within a cloud deployment. These tools can predict failures before they happen and use the metrics from their own response to adjust future mitigation strategies.
2. Resource contention identification
Resource conflicts can lead to overutilization of compute, storage and network capabilities. Overutilization can contribute to slower, weaker performance of cloud services. AI provides real-time analysis and monitoring that exposes any contention points among cloud resources. By continuously consuming this data, particularly machine learning data resources, AI contributes to improvements in efficiency.
Densify, an organization that provides compute resource optimization for Kubernetes, Cloud and AI, provided a deep analysis of a customer's AWS cloud resource utilization to identify cloud performance optimization opportunities. This resource contention identification exercise saved the organization 34% of its expenditures, a savings of $145,000 per month.
3. Predictive resource allocation
Along with resource contention issues, AI can predict the consumption of cloud resources. AI collects and provides this information to IT teams, who can enable AI to configure dynamic scaling to balance cloud cost and performance. Automated scaling enables infrastructure to react to changes in demand quicker than manual administrator processes could. This is especially true in highly volatile environments with rapidly changing needs. AI can even generate, define and refine autoscaling policies based on contention issues with cloud resources.
Major cloud service providers (CSPs) offer specialized AI solutions for autoscaling, including AWS Auto Scaling, Google Cloud Autoscaler, and Azure Autoscale. Social media platform Pinterest used AWS to support its autoscaling capabilities. Today, AWS reports that Pinterest scales log search and analytics to over 1.7 TB while operations costs have reduced by 30%.
4. Performance bottleneck analysis
Root cause analysis is crucial for preventing recurring issues. Unfortunately, the process is often time-consuming and imprecise. AI removes much of the headache and labor of discovering why a performance bottleneck occurred or a scaling scenario failed to satisfy consumer demand. AI correlates log file entries, performance metrics and existing configurations to discover the fundamental problem that led to mediocre performance or downtime.
Be aware that application-level and infrastructure-level bottlenecks require different analyses. If the problem is related to application development, AI offers code analysis to help programmers optimize the application for cloud users. For misconfigured management coding at the infrastructure level, AI can aid in developing YAML or JSON configuration files to ensure proper resource provisioning and scaling.
5. Rightsizing cloud resources
Cloud optimization is about minimizing waste, reducing costs and improving performance. This is also known as rightsizing. AI can provide administrators with plenty of guidance on the best rightsizing configurations. Hybrid and multi-cloud environments often benefit most from this capability, as AI can help determine whether an on-premises deployment is a better choice than a cloud configuration.
AI can also shed light on CSP offerings, finding the service that provides the best cloud cost and performance balance. For example, one provider might offer better pricing on storage services while another provides more feature-rich virtualization capabilities. Rightsizing attempts to eliminate bottlenecking issues and resource overutilization before they occur.
The future of cloud optimization
What future trends should you watch for when it comes to AI-based help with cloud optimization?
Here are a few:
- Increased integration of generative AI solutions in cloud deployment tools.
- Additional integration of AI analysis in edge computing scenarios for faster analysis and action.
- Continued development of agentic AI.
Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget, The New Stack and CompTIA Blogs.
