Tag: scaling

Feed: Recent Announcements. Instance warm-up defines the number of seconds it takes for a newly launched instance to warm up. Once the specified warm-up time expires, the instance is counted toward the aggregated metrics of the Auto Scaling group (ASG), and CAS proceeds with its next iteration of calculations to estimate the number instances required. This prevents the ASG from adding more instances than needed. Before this launch, the instance warm-up time was set to a default value of 300 seconds. Now, you can specify a custom instance warm-up time either when creating a new capacity provider or updating an ... Read More

Feed: Recent Announcements. Capacity providers help you manage compute capacity for containers by allowing the application to define its requirements for how it uses the capacity. With capacity providers, you can define flexible rules for how containerized workloads run on different types of compute capacity, and manage the scaling of the capacity. Capacity providers improve the availability, scalability, and cost of running tasks and services on Amazon ECS. Capacity providers work with both Amazon Elastic Compute Cloud (Amazon EC2) and AWS Fargate. With Amazon EC2, you can create a capacity provider associated with an EC2 Auto Scaling group (ASG). The ... Read More

Feed: AWS Architecture Blog. As businesses race to digitally transform, the challenge is to cope with the amount of data, and the value of that data diminishes over time. The challenge is to analyze, learn, and infer from real-time data to predict future states, as well as to detect anomalies and get accurate results. In this blog post, we’ll explain the architecture for a solution that can achieve real-time inference on streaming data. We’ll also cover the integration of Amazon Kinesis Data Analytics (KDA) with Apache Flink to asynchronously invoke any underlying services (or databases). Managed real-time in-stream data inference ... Read More

Feed: AWS Compute Blog. Author: James Beswick. During the last few weeks, the AWS serverless team has been releasing a wave of new features in the build-up to AWS re:Invent 2020. This post recaps some of the most important releases for serverless developers. re:Invent is virtual and free to all attendees in 2020 – register here. See the complete list of serverless sessions planned and join the serverless DA team live on Twitch. Also, follow your DAs on Twitter for live recaps and Q&A during the event. AWS Lambda We launched Lambda Extensions in preview, enabling you to more easily ... Read More

Feed: AWS Architecture Blog. With the trends to autonomous teams and microservice style architectures, web frontend tiers are challenged to become more flexible and integrate different components with independent architectures and technology stacks. Two scenarios are prominent: Micro-Frontends, where there is a single page application and components within this page are owned by different teams Web portals, where there is a landing page and subsections of the presence are owned by different teams. In the following we will refer to these as components as well. What these scenarios have in common is that they consist of loosely coupled components that ... Read More

Feed: AWS Big Data Blog. Many organizations are processing and analyzing clickstream data in real time from customer-facing applications to look for new business opportunities and identify security incidents in real time. A common practice is to consolidate and enrich logs from applications and servers in real time to proactively identify and resolve failure scenarios and significantly reduce application downtime. Internet of things (IOT) is also driving more adoption for real-time data processing. For example, a connected factory, connected cars, and smart spaces enable seamless sharing of information between people, machines, and sensors. To help ingest real-time data or streaming ... Read More

Feed: Recent Announcements. Previously, gathering performance data on a database required configuration and maintenance of monitoring applications and related resources. Correlating performance data took hours without specialized expertise. To find a query of interest, such as a stuck or long-running SQL query, meant investigating each query one at a time. RDS Performance Insights allows non-experts and experts to identify top SQL loads and from where they originated on a visual dashboard in seconds. Now, RDS Performance Insights supports segmenting the performance data by database name. You can determine if a specific database is generating unusual high load, or is executing ... Read More

Feed: Recent Announcements. Previously, gathering performance data on a database required configuration and maintenance of monitoring applications and related resources. Correlating performance data took hours without specialized expertise. To find a query of interest, such as a stuck or long-running SQL query, meant investigating each query one at a time.   RDS Performance Insights allows non-experts and experts to identify top SQL loads and from where they originated on a visual dashboard in seconds. Now, RDS Performance Insights supports segmenting the performance data by additional dimensions, such as database name, application, and session type. You can determine if a specific ... Read More

Feed: AWS Big Data Blog. In May 2020, we announced the general availability of real-time anomaly detection for Elasticsearch. With that release we leveraged the Random Cut Forest (RCF) algorithm to identify anomalous behaviors in the multi-dimensional data streams generated by Elasticsearch queries. We focused on aggregation first, to enable our users to quickly and accurately detect anomalies in their data streams. However, consider the example data in the following table. timestamp avg. latency region 12:00 pm 3.1 Seattle 12:00 pm 4.1 New York 12:00 pm 5.9 Berlin 12:01 pm 2.6 Seattle 12:01 pm 5.3 New York 12:01 pm 5.8 ... Read More

Feed: Recent Announcements. Before this launch, if your ASG did not confine to a single-AZ or used multiple instance types, the scaling speed was restricted by the minimum scaling step size defined in the capacity provider configuration. Now, CAS can estimate the optimal number of instances to add for such ASGs, similar to how it estimates for single AZ and instance type ASGs, making the scaling more responsive. With CAS, the scaling policy of your ASG is managed by Amazon ECS through a capacity provider. You can configure the capacity provider to enable managed scaling of the ASG, reserve excess ... Read More