Popular Public Cloud Services
Amazon EKS, Azure AKS, and Google GKE are popular managed Kubernetes services that provide an easy way to deploy, manage, and scale containerized applications in the cloud. With a wide variety of readily available instance types, they enable DevOps teams to easily scale up or down to handle changes in requirements or popularity spikes, reducing the risk of resource shortages when operating.
Benefits of Federator.ai employed for Optimization of Public Cloud Services
Container adoption can introduce operation agility but also significant operational overhead, especially when managing compute resources across on-premises, hybrid cloud, MultiCloud, or edge architectures. To address this challenge, Federator.ai offers an AI-powered solution that optimizes container resources in public clouds.
Multi-layer Visibility and Optimization
Federator.ai leverages the collected operational metadata from the multi-layer infrastructure operations to provide ongoing just-in-time recommendations for optimized resource orchestration based on its AI-powered workload predictions.
Resource Utilization and Costs Improvement
Federator.ai's application-aware resource orchestration auto-scales container resources based on real-time application demands. It identifies workload patterns and suggests the most cost-effective combination of instance types and numbers for operations, which improves resource utilization, eliminates over-provisioning, reduces costs, and promotes environmentally sustainable operations.
Performance Assurance and Resilience
Federator.ai uses its patented cascade causal analysis to identify intricate correlations between hardware/cloud resources, Kubernetes layers (cluster, node, pod), and applications. This allows for an efficient allocation of resources to mission-critical applications, ensuring smooth performance and resilience while minimizing resource usage.
Federator.ai's operation optimization is a continuous virtuous circle powered by automation. It begins with a precise interpretation of operational metadata and accurate prediction of application workloads, followed by prescriptive recommendations of well-fitted resources - all done automatically after setting. Additionally, users can access Federator.ai's resource orchestration script for optimization with compliance with company policies.