Kategorie: Amazon AWS

AWS Network Firewall updates default drop action for improved connection reliability

AWS Network Firewall now uses “Application drop established (server-directed only)” as the default stateful action for all newly created firewall policies, replacing the previous default of “Application drop established (bidirectional)” (formerly named “Application layer drop established”). No action is required to benefit from this change when creating new policies. AWS Network Firewall is a managed service that lets you deploy network protections across your Amazon VPCs. Previously, the “Application drop established (bidirectional)” default could silently drop legitimate server-to-client TCP packets, such as window updates, keep-alives, and resets — causing intermittent connection failures that were difficult to diagnose. With the safer default now in place, new policies avoid this issue. If your existing environment requires “Application drop established (bidirectional)” to support post-quantum cryptography (PQC) fragmented TLS handshakes, refer to our documentation for guidance on on switching to “Application drop established (server-directed only)” or adding the “to_server” flag to your TCP drop rules so legitimate flow control packets are not blocked. This update is available in all AWS Regions where AWS Network Firewall is offered. To get started, see Managing evaluation order for Suricata compatible rules in the AWS Network Firewall service documentation.
Quelle: aws.amazon.com

Amazon CloudWatch Synthetics now supports multilocation canaries

Today, Amazon CloudWatch Synthetics announces support for multilocation canaries, allowing developers and site reliability engineers to run the same canary across multiple AWS Regions simultaneously from a single point of management. Previously, monitoring application availability from multiple geographic locations required creating and managing separate canaries in each Region, adding operational overhead and increasing the risk of configuration drift. With multilocation canaries, you create and manage a canary in one primary Region, and CloudWatch Synthetics automatically replicates it to the additional Regions you choose, consolidating all run data, metrics, and artifacts in the primary Region.
Multilocation canaries help you ensure consistent user experience worldwide, identify region-specific performance bottlenecks, and validate that third-party dependencies like CDNs and payment processors work across all locations. Replica canaries run independently, giving you resilient monitoring coverage across geographic locations. You can also configure alarms that activate only when issues are detected from multiple locations, increasing alert confidence and helping your team focus on real customer-impacting problems. Amazon CloudWatch Synthetics multilocation canaries are available in all AWS commercial Regions that support CloudWatch Synthetics. You can upgrade existing single-region canaries to multilocation by adding replica Regions without recreating them. For more information about regional availability, see the AWS Region table.
To learn more about CloudWatch Synthetics, see Using synthetic monitoring in the Amazon CloudWatch User Guide. To get started, visit the Amazon CloudWatch product page.
Quelle: aws.amazon.com

Amazon MQ for RabbitMQ now supports private networking connectivity

Amazon MQ for RabbitMQ now supports private networking, enabling your brokers to connect to private resources in your VPC without exposing those resources publicly.. This helps you meet your security and compliance requirements when your brokers need to reach private identity providers (such as LDAP and OAuth 2.0), other Amazon MQ for RabbitMQ brokers, or self-hosted RabbitMQ brokers. Previously, this connectivity for RabbitMQ Federation, Shovel, or authentication required Network Load Balancer and NAT Gateway workarounds. Amazon MQ establishes this connectivity using Amazon VPC Lattice, AWS Resource Access Manager (AWS RAM), and AWS PrivateLink, and manages the underlying infrastructure on your behalf. To get started, create a VPC Lattice resource gateway, package your resource configurations into an AWS RAM resource share, and associate it with your broker. Private networking is available only for Amazon MQ for RabbitMQ brokers, in all AWS Regions where Amazon VPC Lattice is available. To learn more, see Private networking in the Amazon MQ Developer Guide and the Amazon MQ pricing page.
Quelle: aws.amazon.com

Amazon EC2 G7 instances are now generally available

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) G7 instances, accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances deliver up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6.
You can use G7 instances for AI inference workloads such as language translation, video and image analysis, speech recognition, and recommender systems. Additionally, G7 instances also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics, and game streaming, as well as data analytics workloads such as large-scale data processing pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU, custom Intel Xeon 6 processors, and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth.
You can start using Amazon EC2 G7 instances today in two AWS Regions: US East (Ohio) and US West (Oregon). You can purchase G7 instances as On-Demand Instances, as part of Savings Plans, or Spot Instances. 
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit this blog post and the G7 instance page.
Quelle: aws.amazon.com

Amazon ECS announces faster service auto scaling

Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates. Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Quelle: aws.amazon.com

Amazon MSK Express brokers now support Intelligent Rebalancing on existing clusters

Amazon MSK Provisioned clusters with Express brokers now support Intelligent Rebalancing on all existing clusters, at no additional cost. Previously available only on newly created clusters, Intelligent Rebalancing is now available on all MSK Provisioned clusters running Express brokers, making it effortless for customers to benefit from automatic partition balancing when scaling their Express-based clusters up or down.
Intelligent Rebalancing maximizes the capacity utilization of MSK Express-based clusters by optimally rebalancing Kafka resources for better performance, eliminating the need for customers to manage partitions themselves or via third-party tools. Intelligent Rebalancing performs these operations up to 180 times faster compared to Standard brokers. Clusters are continuously monitored for resource imbalance or overload based on intelligent Amazon MSK defaults to maximize cluster performance. When required, brokers are efficiently scaled without affecting cluster availability for clients to produce and consume data.
Intelligent Rebalancing is now available on all MSK Provisioned clusters with Express brokers in all AWS Regions where Express brokers are available. To learn more, see the Amazon MSK Developer Guide.
Quelle: aws.amazon.com

Announcing the general availability of a new AWS Local Zone in Hanoi, Vietnam 

Today, AWS announces the general availability of a new Local Zone in Hanoi, Vietnam, bringing AWS infrastructure closer to end users. This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon Simple Storage Service (Amazon S3) and Amazon Elastic Block Store (Amazon EBS) Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally.
AWS Local Zones are AWS infrastructure deployments that extend core services, such as compute, storage, networking, and other select services, closer to metropolitan areas worldwide. AWS Local Zones help you achieve single-digit millisecond latency for end-user workloads, meet data residency requirements, support AI/ML inference workloads, and accelerate migration and modernization of legacy applications to the cloud, all while maintaining consistent AWS APIs, tools, and services as AWS Regions. AWS Local Zones are available in more than 30 metropolitan areas worldwide. 
The Hanoi Local Zone supports Amazon Elastic Compute Cloud (Amazon EC2) with C7i, M7i, and R7i instances, Amazon S3 with the One Zone-Infrequent Access storage class, Amazon EBS with Local Snapshots and volume types gp3, gp2, io1, sc1, and st1, Amazon Elastic Container Service (Amazon ECS), Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Virtual Private Cloud (Amazon VPC), AWS Direct Connect, and Application Load Balancer. 
To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API. For pricing information, visit the AWS Local Zones pricing page. To learn more, visit the AWS Local Zones overview page.  
Quelle: aws.amazon.com

Amazon SageMaker AI Announces New observability capability For Inference Endpoints

Amazon SageMaker AI’s new observability capability allows customers to operate production generative AI inference workloads with confidence by providing comprehensive visibility into token performance, GPU health, inference component placement, and autoscaling behavior. It takes away the manual work of searching CloudWatch for per-endpoint metrics, correlating latency spikes with GPU saturation or KV cache exhaustion and diagnosing why scaling operations are slow. This capability tracks inference performance metrics in real-time, including Time to First Token, inter-token latency, queue depth, and tokens per second, and surfaces them alongside infrastructure health so customers can identify and resolve issues in minutes rather than hours.
SageMaker AI detailed observability transforms how customers monitor and optimize their inference fleet. The new pre-built SageMaker AI Insights dashboard in Amazon CloudWatch gives customers token latency, GPU utilization, inference component copy counts, scaling events, and cold start breakdowns in a single view with OpenTelemetry native metrics published automatically, no instrumentation required. This allows teams to quickly diagnose TTFT degradation, verify availability zone compliance, and tune autoscaling policies. Customers who have standardized on observability tools like Grafana can connect directly using the regional PromQL endpoint and import a pre-configured dashboard template. This capability helps customers self-serve operational issues and maximize the performance of their AI investments.
SageMaker AI Inference observability is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), US West (N. California), Canada (Central), South America (São Paulo), Europe (Ireland), Europe (Frankfurt), Europe (London), Europe (Stockholm), Europe (Zurich), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Asia Pacific (Seoul), and Asia Pacific (Jakarta). To learn more, visit the Documentation and Amazon SageMaker AI webpage.
Quelle: aws.amazon.com

all-MiniLM-L12-v2 for semantic search and sentence similarity is now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure.
all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Ministral-3-14B-Instruct for multimodal reasoning and agentic AI is now available in Amazon SageMaker JumpStart

Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure.
Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic. 
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com