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Amazon Route 53 launches capacity utilization metric for Resolver endpoints

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Starting today, you can enable Amazon CloudWatch metric (ResolverEndpointCapacityStatus) to monitor the status of the query capacity for Elastic Network Interfaces (ENIs) associated with your Route 53 Resolver endpoint in Amazon Virtual Private Cloud (VPC). The new metric enables you to quickly view whether the Resolver endpoint is at the risk of meeting the service limit for query capacity, and take remediation steps like instantiating additional ENIs to meet the capacity needs.

Before today, you could enable CloudWatch to monitor the number of DNS queries that were forwarded by Route 53 Resolver endpoints, over a default five-minute interval, and make further estimations on when your endpoints will meet the query limits. With this launch, you can now enable the new metric to get direct alerts on the current status of your Resolver endpoint capacity, without requiring you to make additional estimations for calculating capacity of each endpoint. The status is reported for each Resolver endpoint, indicating whether the endpoint is operating within the normal capacity limit (0 – OK), has at least one ENI exceeding 50% capacity utilization (1 – Warning), or has at least one ENI exceeding 75% capacity utilization (2 – Critical). The new metric simplifies capacity management for Route 53 Resolver endpoints by providing clear, actionable signals for scaling decisions, without requiring additional analysis on the query volume.

To learn more about the launch, read the documentation or visit the Route 53 Resolver page . There is no charge for the metric, although you will incur charges for usage of Resolver endpoints

Amazon Connect can now include agent activities from third-party applications when evaluating agent performance

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Amazon Connect can now integrate agent activities from third-party applications as Connect Tasks, which can be evaluated alongside work completed in Connect, providing managers with a unified application for quality management. You can programmatically ingest activities from third-party applications (such as application processing, social media posts, etc.) as completed Tasks within Connect, capturing details relevant for performance evaluation as Task attributes. Managers can then evaluate these external activities, alongside native Connect interactions to get a unified view of agent performance within Connect dashboards.

This feature is available in all regions where Contact Lens performance evaluations are already available. To learn more, please visit our documentation and our webpage . For information about Contact Lens pricing, please visit our pricing page .
 

Announcing Amazon SageMaker HyperPod training operator

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Today, we’re announcing general availability of Amazon SageMaker HyperPod training operator, a purpose-built Kubernetes extension for resilient foundation model training on HyperPod.

Amazon SageMaker HyperPod empowers customers to accelerate AI model development across hundreds or thousands of GPUs with built-in resiliency, decreasing model training time by up to 40%. As training clusters expand, recovery from training interruptions becomes increasingly disruptive. Failure recovery traditionally requires a complete job restart across all nodes when even a single training process fails, resulting in additional downtime and increased costs. Moreover, identifying and resolving critical training issues such as stalled GPUs, low training throughput, and numerical instabilities, typically requires complex custom monitoring code, further extending development timelines and delaying time to market.

With the HyperPod training operator, customers can further enhance training resilience for Kubernetes workloads. Instead of a full job restart when failures occur, the HyperPod training operator performs surgical recovery, selectively restarting only the affected training resources for faster recovery from faults. It also introduces a customizable hanging job monitoring capability to help overcome problematic training scenarios including stalled training batches, non-numeric loss values, and performance degradation through simple YAML configurations. Getting started is simple: create a HyperPod cluster, install the training operator add-on, optionally define custom recovery policies for hanging jobs, and launch training.

This release is generally available in all AWS Regions where SageMaker HyperPod is currently supported.

See the documentation to learn more.

AWS Global Accelerator now supports endpoints in two additional AWS Regions

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Starting today, AWS Global Accelerator supports application endpoints in two additional AWS Regions, AWS Mexico (Central) Region and Asia Pacific (Malaysia) Region, expanding the number of supported AWS Regions to thirty one.

AWS Global Accelerator is a service that is designed to help you improve the availability, security, and performance of your internet-facing applications. By using the congestion-free AWS network, end-user traffic to your applications benefits from increased availability, DDoS protection at the edge, and higher performance relative to the public internet. Global Accelerator provides static IP addresses that act as fixed entry endpoints for your application resources in one or more AWS Regions, such as your Application Load Balancers, Network Load Balancers, Amazon EC2 instances, or Elastic IPs. Global Accelerator continually monitors the health of your application endpoints and offers deterministic fail-over for multi-region workloads without any DNS dependencies.

To get started, visit the AWS Global Accelerator website and review its documentation .
 

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