Cloud Computing Köln

Amazon FSx now integrates with AWS Secrets Manager for enhanced management of Active Directory credentials

Amazon FSx now integrates with AWS Secrets Manager, enabling enhanced protection and management of the Active Directory domain service account credentials for your FSx for Windows File Server file systems and FSx for NetApp ONTAP Storage Virtual Machines (SVMs). Previously, if you wanted to join your FSx for Windows file system or FSx for ONTAP SVM to your Active Directory domain for user authentication and access control, you needed to specify the username and password for your service account in the Amazon FSx Console, Amazon FSx API, AWS CLI, or AWS CloudFormation. With this launch, you can now specify an AWS Secrets Manager secret containing the service account credentials, enabling you to strengthen your security posture by eliminating the need to store plain text credentials in application code or configuration files, and aligning with best practices for credential management. Additionally, you can use AWS Secrets Manager to rotate your Active Directory credentials and consume them when needed in FSx workloads. You can now use AWS Secrets Manager to store your domain join service credentials for all FSx for Windows file systems and FSx for ONTAP Storage Virtual Machines in all AWS Regions where they are available. For more information, see Amazon FSx for Windows File Server documentation and Amazon FSx for NetApp ONTAP documentation.
Quelle: aws.amazon.com

Amazon CloudWatch Database Insights expands anomaly detection in on-demand analysis

Amazon CloudWatch Database Insights now detects anomalies on additional metrics through its on-demand analysis experience. Database Insights is a monitoring and diagnostics solution that helps database administrators and application developers optimize database performance by providing comprehensive visibility into database metrics, query performance, and resource utilization patterns. The on-demand analysis feature utilizes machine learning to help identify anomalies and performance bottlenecks during the selected time period, and gives advice on what to do next. The Database Insights on-demand analysis feature now offers enhanced anomaly detection capabilities. Previously, database administrators could analyze database performance and correlate metrics based on database load. Now, the on-demand analysis report also identifies anomalies in database-level and operating system-level counter metrics for the database instance, as well as per-SQL metrics for the top SQL statements contributing to database load. The feature automatically compares your selected time period against normal baseline performance, identifies anomalies, and provides specific remediation advice while reducing mean time to diagnosis. Through intuitive visualizations and clear explanations, you can quickly identify performance issues and receive step-by-step guidance for resolution. You can get started with on-demand analysis by enabling the Advanced mode of CloudWatch Database Insights on your Amazon Aurora or RDS databases using the AWS management console, AWS APIs, or AWS CloudFormation. Please refer to RDS documentation and Aurora documentation for information regarding the availability of Database Insights across different regions, engines, and instance classes.
Quelle: aws.amazon.com

Announcing New EC2 R8a Memory-Optimized Instances

AWS is announcing the general availability of new memory-optimized Amazon EC2 R8a instances. R8a instances, feature 5th Gen AMD EPYC processors (formerly code named Turin) with a maximum frequency of 4.5 GHz, deliver up to 30% higher performance, and up to 19% better price-performance compared to R7a instances. R8a instances deliver 45% more memory bandwidth compared to R7a instances, making these instances ideal for latency sensitive workloads. Compared to Amazon EC2 R7a instances, R8a instances provide up to 60% faster performance for GroovyJVM, allowing higher request throughput and better response times for business-critical applications. Built on the AWS Nitro System using sixth generation Nitro Cards, R8a instances are ideal for high performance, memory-intensive workloads, such as SQL and NoSQL databases, distributed web scale in-memory caches, in-memory databases, real-time big data analytics, and Electronic Design Automation (EDA) applications. R8a instances offer 12 sizes including 2 bare metal sizes. Amazon EC2 R8a instances are SAP-certified, and providing 38% more SAPS compared to R7a instances. R8a instances are available in the following AWS Regions: US East (N. Virginia), US East (Ohio), and US West (Oregon) regions. To get started, sign in to the AWS Management Console. Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 R8a instance page.
Quelle: aws.amazon.com

Amazon CloudWatch Application Signals adds AI-powered Synthetics debugging

Amazon CloudWatch Application Signals Model Context Protocol or MCP Server for Application Performance Monitoring (APM) now integrates CloudWatch Synthetics canary monitoring directly into its audit framework, enabling automated, AI-powered debugging of synthetic monitoring failures. DevOps teams and developers can now use natural language questions like ‘Why is my checkout canary failing?’ in compatible AI assistants such as Amazon Q, Claude, or other supported assistants to utilize the new AI-powered debugged capabilities and quickly distinguish between canary infrastructure issues and actual service problems, addressing the significant challenge of extensive manual analysis in maintaining reliable synthetic monitoring. The integration extends Application Signals’ existing multi-signal (services, operations, SLOs, golden signals) analysis capabilities to include comprehensive canary diagnostics. The new feature automatically correlates canary failures with service health metrics, traces, and dependencies through an intelligent audit pipeline. Starting from natural language prompts from users, the system performs multi-layered diagnostic analysis across six major areas: Network Issues, Authentication Failures, Performance Problems, Script Errors, Infrastructure Issues, and Service Dependencies. This analysis includes automated comparison of HTTP Archive or HAR files, CloudWatch logs analysis, S3 artifact examination, and configuration validation, significantly reducing the time needed to identify and resolve synthetic monitoring issues. Customers can then access these insights through natural language interactions with supported AI assistants. This feature is available in all commercial AWS regions where Amazon CloudWatch Synthetics is offered. Customers will need access to a compatible AI agent such as Amazon Q, Claude, or other supported AI assistants to utilize the AI-powered debugging capabilities. To learn more about implementing AI-based debugging for your synthetic monitoring, visit the CloudWatch Application Signals MCP Server documentation.
Quelle: aws.amazon.com