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Introduction Amazon Elastic Container Service (Amazon ECS) has now enhanced its functionalities by integrating support for Amazon Elastic Block Store (Amazon EBS) volume attachment to Amazon ECS tasks. This feature simplifies using Amazon ECS and AWS Fargate with Amazon EBS. Amazon ECS facilitates seamless provisioning and attachment of EBS volumes to ECS tasks on both Fargate and Amazon Elastic Cloud Compute (Amazon EC2) platforms. In Amazon ECS tasks, you have the flexibility to select EBS volume attributes, such as size, type, IOPS, and throughput, tailoring the storage to meet the specific needs of your application. Additionally, the capability to create volumes from snapshots allows for the rapid deployment of new tasks with pre-populated data. With this feature, Amazon ECS streamlines the deployment of storage-heavy and data-intensive applications, such as ETL processes, media transcoding, and machine learning (ML) inference. For a comprehensive understanding of integrating Amazon ECS with Amazon EBS, see Channy Yun’s launch post, which offers detailed guidance on getting started with this integration. In this post, we discuss performance benchmarking results for Fargate tasks using EBS volumes. The goal aims to assess the performance profiles of various EBS volume configurations under simulated workloads. The insights garnered from this analysis can assist you in identifying the optimal storage configurations for I/O intensive workloads. For context, the data and observations presented in this post are specific to the Oregon Region, reflecting the state of the Fargate’s On-Demand data plane as observed in February 2023. Note that the situation might have changed, offering a different landscape today. EBS volume types Amazon EBS offers a range of block storage volumes, leveraging both Solid State Drive (SSD) and Hard Disk Drive (HDD) technologies to cater to different workload requirements: General Purpose SSD volumes (gp2 and gp3) Provisioned IOPS SSD volumes (io1 and io2 Block Express) Throughput Optimized HDD volumes (st1) Cold HDD volumes (sc1) General Purpose SSD volumes are the most commonly used block storage volume. Backed by solid-state drives, these volumes offer a balanced performance for a broad range of transactional workloads, including boot volumes, medium-sized databases, and low-latency interactive applications. They strike an optimal balance between cost and performance, making them suitable for a variety of use cases that demand consistent, moderate IOPS with reliable throughput. Provisioned IOPS SSD io1 and io2 volumes feature solid-state drives, marking them as Amazon EBS’s storage solutions for high IOPS and low latency needs. Both are tailored for critical applications that demand consistent, rapid access, promising IOPS reliability at a 99.9% rate, suitable for high-performance databases and applications. However, io2 differentiates itself by offering increased durability, larger capacity options, and consistent latency. However, both volumes serve distinct needs depending on the specific requirements of the workload, making sure of flexibility in choice. Throughput Optimized HDD st1 volumes are designed to offer low-cost magnetic storage prioritized for throughput over IOPS. These volumes align with the needs of workloads that benefit from large, sequential reads and writes, making them ideal for processes such as big data analytics, log processing, and data warehousing. Cold HDD sc1 volumes, similar to st1 volumes, focus on throughput but at a more economical rate and with a lower threshold. Best suited for less frequently accessed, sequential cold data, these volumes represent a lowest-cost solution for storage needs that don’t demand constant access. Testing methodology We tested each EBS volume type across multiple Fargate task sizes with XFS. The baseline EBS volume IOPS and throughput available for a Fargate task depend on the total CPU units you request. The difference in storage performance is clear in the results. For example, tasks with 16 vCPUs provide higher IOPS and throughput as compared to tasks with 0.25 vCPUs. To make sure of a thorough examination, we explored a spectrum of Fargate task sizes, ranging from tasks allocated with 0.25 vCPUs up to those with 16 vCPUs, across the following configurations: .25 vCPU | 1 GB .5 vCPU | 2 GB 1 vCPU | 4 GB 2 vCPU | 6 GB 4 vCPU | 8 GB 8 vCPU | 16 GB 16 vCPU | 32 GB Our testing methodology for General Purpose SSD and Provisioned IOPS SSD volumes involved conducting 16 KB random read and write operations, adhering to the guidelines specified in the EBS documentation. For tasks equipped with Throughput Optimized HDD or Cold HDD volumes, our approach entailed executing 1 MiB sequential read and write operations to better gauge their performance under workload conditions typical for these storage types. By repeating each test three times and calculating the mean values, we aimed to make sure of the reliability and accuracy of our performance measurements. General purpose SSD – gp3 volumes Given the versatility and price-to-performance ratio of gp3, we expect this volume type to be the most commonly used block storage for Fargate tasks. gp3 volumes deliver a baseline performance of 3,000 IOPS and 125 MiB/s at any volume size. Fargate supports gp3 volumes with support for a maximum of 16,000 IOPS and 1,000 MiB throughput. We performed tests on gp3 volumes configured with 2,000 GiB size, 16,000 IOPS, and 1000 MiB/s throughput to guarantee maximum storage performance. General purpose SSD – gp3 volumes Given the versatility and price-to-performance ratio of gp3, we expect this volume type to be the most commonly used block storage for Fargate tasks. gp3 volumes deliver a baseline performance of 3,000 IOPS and 125 MiB/s at any volume size. Fargate supports gp3 volumes with support for a maximum of 16,000 IOPS and 1,000 MiB throughput. We performed tests on gp3 volumes configured with 2,000 GiB size, 16,000 IOPS, and 1000 MiB/s throughput to guarantee maximum storage performance. We learned Fargate offers consistent IOPS performance across most task sizes. Tasks with 0.25 vCPU and 1 GB memory are an outlier in this group, as they do not deliver the maximum 16,000 IOPS. Task sizes from 1 vCPU onward achieve the maximum configured IOPS. Tasks with 0.25 vCPU couldn’t go beyond 200 MiB/s and 150 MiB/s in read and write tests respectively. General Purpose SSD – gp2 volumes We recommend customers opt for gp3 volumes over gp2 for several reasons. Firstly, gp3 volumes allow for the provisioning of IOPS independently from storage capacity, offering more flexibility. Secondly, they are more cost-effective, with a 20% lower price per GB compared to gp2 volumes. The gp2 volume performance relies on a burst bucket model, where the size of the volume dictates its baseline IOPS. This baseline determines the rate at which the volume accumulates throughput credits. For those customers with specific needs, Fargate continues to support gp2 volumes. Our decision to include gp2 volumes in our benchmarking was straightforward, as our testing setup was already compatible. We benchmarked gp2 volumes at a size of 6,000 GiB. At this size, gp2 volumes can achieve 16,000 IOPS, the maximum for gp2, due to the volume size proportionally influencing the IOPS allocation. The IOPS performance on gp2 volumes was consistent across all task sizes except 0.5 and 0.25 vCPUs. Tasks with 1 vCPU and larger achieved maximum provisioned throughput of 16,000 IOPS. Throughput performance on gp2 was very similar to gp3 volumes. The test results offer another proof of why customers should prefer gp3 over gp2. Provisioned IOPS SSD – io1 volumes Amazon EBS io1 volumes are ideal for both IOPS-intensive and throughput-intensive workloads that require low latency and have moderate durability requirements or include built-in application redundancy. io1 and io2 volume types provide higher throughput and IOPS compared to gp3 volumes. We performed tests on io1 volumes configured with 2,000 GiB size and 64,000 IOPS Only tasks with 8 or more vCPUs achieved more than 20,000 IOPS. Even though the io1 volumes attached to the tasks supported up to 64,000 IOPS, none of the tasks approached the maximum IOPS mark in our tests. Considering these results, gp3 may turn out to be more cost-effective storage for tasks with fewer than 8 vCPUs. Tasks with io1 volumes reported more I/O throughput as compared with gp3. For applications that need higher throughput and IOPS, io1 volumes are more suitable. All tasks, except 0.25 vCPU, achieved at least 300 MiB/s of throughput. Compare this to gp3, which achieved a maximum of 260 MiB/s. Provisioned IOPS SSD – io2 Block Express volumes Amazon EBS io2 Block Express offers the highest performance block storage in the cloud with 4x higher throughput, IOPS, and capacity than gp3 volumes, along with sub-millisecond latency. io2 Block Express is designed to provide 4,000 MB/s throughput per volume, 256,000 IOPS/volume, up to 64 TiB storage capacity, and 1,000 IOPS per GB as well as 99.999% durability. The io2 volumes we used in benchmarking had 2,000 GiB size and 10,000 IOPS. io2 volumes attained more IOPS on Fargate than io1 volumes on tasks with more vCPUs. However, the IOPS performance of io1 and io2 volumes is identical for tasks with less than 8 vCPUs. Even tasks with 8 and 16 vCPUs achieved about 40,000 IOPS on io2 volumes with 10,000K provisioned IOPS. Note that random write performance on tasks with io2 volumes was much higher than io1, but it’s only applicable for larger tasks. The throughput scaling with task size observed with io2 volumes is similar to that of io1, with io2 volumes demonstrating higher write throughput. Note that in most scenarios, io2 is a more advantageous choice over io1. Although both volume types start at the same price point, io2’s tiered IOPS pricing model makes it a more cost-effective option for configurations requiring high IOPS. Throughput Optimized HDD – st1 Throughput Optimized HDD (st1) volumes provide low-cost magnetic storage that defines performance in terms of throughput rather than IOPS. This volume type is a good fit for large, sequential workloads, such as Amazon EMR, ETL, data warehouses, and log processing. st1 volumes offer low-cost storage for workloads that need large and sequential I/O. Like gp2, st1 uses a burst bucket model for performance. Volume size determines the baseline throughput of your volume, which is the rate at which the volume accumulates throughput credits. st1 volumes provide burst throughput of up to 500 MiB/s. We configured st1 volumes with 13,000 GiB size, which results in a base throughput of 500 MiB/s. st1 volumes are throughput optimized, throughput is a more appropriate measurement of performance. We’ve included IOPS results to be consistent. To summarize, all tasks with over 1 vCPU attain about 500 IOPS. st1 offers consistent throughput across most task sizes. Although io1 and io2 provide over 500 MiB/s throughput on tasks with 8 and 16 vCPUs, st1 offers about 500 MiB/s throughput on most task sizes. This makes st1 better suited for workloads that need higher throughput with smaller task sizes. Cold HDD – sc1 volumes Cold HDD (sc1) volumes provide low-cost magnetic storage that, like st1, defines performance in terms of throughput rather than IOPS. sc1 volumes have lower throughput than st1, making them ideal for large, sequential cold-data workloads. sc1 (such as gp2 and st1) also uses a burst bucket model. Volume size determines the baseline throughput. We maxed the size of sc1 volumes to 16 TiB storage in order to guarantee the maximum throughput of 192 MiB/s. All sc1 volumes have burst throughput of 250 MiB/s. Our tests showed that sc1 volumes achieved about half of the IOPS when compared to st1 volumes. Once again, tasks with one and more vCPUs had consistent IOPS performance. sc1 volumes reported about half of the throughput when compared to st1 volumes. Given that sc1 volumes cost a third of a similarly sized st1 volume, sc1 volumes are great for workloads that need infrequent access to data. Conclusion This post reviewed Amazon EBS performance across different Fargate task sizes. It found that for the majority of workloads on Fargate, gp3 volumes, aptly named for their general-purpose use, are appropriate. However, it is advised against using io1 and io2 volumes for tasks requesting 0.25, 0.5, or 1 vCPU due to insufficient CPU cycles to leverage their potential for more than 30,000 IOPS and 300 MiB throughput. Instead, these high-performance volumes are recommended for workloads requiring significant IOPS and throughput. For tasks needing sequential I/O, st1 volumes, or the more economical sc1 volumes, are also beneficial. View the full article

Introduction Amazon Elastic Container Service (Amazon ECS) is a container orchestration service that manages the lifecycle of billions of application containers on AWS every week. One of the core goals of Amazon ECS is to remove overhead burden from human operators. Amazon ECS watches over your application containers 24/7, and can respond to unexpected changes faster and better than any human can. Amazon ECS reacts to undesired changes, such as application crashes and hardware failures by continuously attempting to self-heal your application container deployments back to your desired state. There are also external factors such as traffic spikes that can cause an application brown out. This can be more challenging to handle. This post dives deep into recent changes to how Amazon ECS handles task health issues and task replacement, and how these changes increase the availability of your Amazon ECS orchestrated applications. Task health evaluation Amazon ECS evaluates the health of a task based on a few criteria: First, for a task to be healthy all containers that are marked as essential must be running. Every Amazon ECS task must have at least one essential container. Best practice containers run a single application process, and if that process ends because of a critical runtime exception, then the container stops. If that stopped container was marked as essential, then the entire task is considered to be unhealthy and the task must be replaced. You can use the Amazon ECS Task Definition to configure an optional internal health check command that the Amazon ECS agent runs inside the container periodically. This command is expected to return a zero exit code that indicates success. If it returns a non-zero exit code, then that indicates failure. The container is considered unhealthy and an unhealthy essential container causes the task to be considered unhealthy, which causes Amazon ECS to replace the task. You can use the Amazon ECS service to configure attachments between your application container and other AWS services. For example, you can connect your container deployment to an Amazon Elastic Load Balancer (ELB) or AWS Cloud Map. These services perform their own external health checks. For example, ELB periodically attempts to open a connection to your container and send a test request. If it isn’t possible to open that connection, your container returns an unexpected response, or your container takes too long to respond, then the ELB considers the target container to be unhealthy. Amazon ECS also considers this external health status when deciding whether an Amazon ECS task is healthy or unhealthy. An unhealthy ELB health check causes the task to be replaced. For a task to be healthy, all sources of health status must evaluate as healthy. If any of the sources return an unhealthy status, then the Amazon ECS task is considered unhealthy and it will be replaced. Task replacement behavior Replacing an Amazon ECS task is something that happens in two main circumstances: During a fresh deployment triggered by the UpdateService API call. Any existing tasks that are part of the previous deployment must be replaced by new tasks that are part of the new deployment. When an existing task inside an active deployment becomes unhealthy. Unhealthy tasks must be replaced in order to maintain the desired count of healthy tasks. From early on in the history of Amazon ECS, the behavior of task replacement during rolling deployments has been configurable using two properties of the Amazon ECS service: maximumPercent – This controls how many additional tasks Amazon ECS can launch above the service’s desired count. For example, if the maximumPercent is 200% and the desired count for the service is eight tasks, then Amazon ECS can launch additional tasks up to a total of 16 tasks. minimumHealthyPercent – This controls the percentage that an Amazon ECS service is allowed to go below the desired count during a deployment. For example, if minimumHealthyPercent is 75% and the desired count for the service is eight tasks, then Amazon ECS can stop two tasks, reducing the service deployment down to six running tasks. The maximumPercent and minimumHealthyPercent have functioned for many years as efficient controls for fine tuning the behavior of rolling deployments when running Amazon ECS tasks on Amazon Elastic Compute Cloud (Amazon EC2) capacity. However, these deployment controls don’t make as much sense in a world where more and more Amazon ECS users are choosing serverless AWS Fargate capacity. In most cases, modern applications don’t require Amazon ECS to go below the desired count of running tasks during a rolling deployment or reduce the number of additional tasks being launched during a rolling deployment, because AWS Fargate utilization isn’t constrained by how many underlying Amazon EC2 instances you have registered into your cluster. Additionally, the maximumPercent and minimumHealthyPercent controls were originally ignored when it came to replacing unhealthy tasks. If tasks became unhealthy, then your service’s desired count could dip well below the threshold defined by minimumHealthyPercent. For example, if you were running eight tasks and four of them became unhealthy, then Amazon ECS would terminate the four unhealthy tasks and launch four replacement tasks. The number of running tasks would temporarily dip to 50% of the desired count. Updates to how Amazon ECS replaces unhealthy tasks As of October 20, 2023, Amazon ECS now uses your maximumPercent whenever possible when replacing unhealthy tasks. Let’s look at a few scenarios to understand how this works: Crashing tasks You’re running a service with a desired count of eight tasks and maximum healthy percent of 200%. Four of your eight tasks encounter critical runtime exceptions. Their processes crash and exit, which causes an essential container to exit. Amazon ECS observes that four of the eight tasks have gone unhealthy because their essential container exited. Unfortunately, Amazon ECS can’t avoid the healthy percentage dipping below 100% because the unhealthy container crashed. The running task count dips to 50% of the desired count briefly, but Amazon ECS launches four replacement tasks as quickly as possible to bring the number of running tasks back up to the desired count of eight tasks. Frozen tasks You’re running a service with a desired count of eight tasks and maximum healthy percent of 200%. Because of an endless loop in your code four of your eight tasks freeze up, but the processes stay running. The attached load balancer that is sending health check requests to the service observes that the target container is no longer responsive to health check requests, so it marks the target as unhealthy. Amazon ECS considers those four frozen tasks to be unhealthy. The maximum percent for the service allows it to go up to 16 tasks. Amazon ECS launches four additional replacement tasks for the four unhealthy tasks, making a total of 12 running tasks. Once the four additional tasks have become healthy, Amazon ECS stops the four unhealthy tasks, which brings the running task count back down to the desired count of eight tasks. Overburdened tasks You’re running a service with a desired count of eight tasks and maximum healthy percent of 150%. The service has autoscaling rules attached to it. It also has a load balancer attached to it, and a large spike of traffic arrives via the load balancer. The spike of traffic is so large that response time from the task rises dramatically. As a result of high response time, the load balancer health check fails and the ELB marks all eight targets as unhealthy. The ELB fails open and continues distributing traffic to all the targets as there are no healthy targets in the load balancer. Amazon ECS observes that all eight tasks are unhealthy. As a result, Amazon ECS wants to replace these unhealthy tasks. The maximum percent of 150% allows the service to go up to 12 running tasks. Therefore, Amazon ECS avoids stopping the unhealthy running tasks immediately. Instead, it launches four replacement tasks in parallel with the existing eight unhealthy tasks. Fortunately these four additional tasks give the ELB more targets to distribute traffic across, and all 12 of the running tasks stabilize in health as they are now able to handle the incoming traffic without timing out. Amazon ECS observes that there are now 12 healthy running tasks. Simultaneously with this, an Application Auto Scaling rule has kicked in based on seeing high CPU utilization by the original eight running tasks. The rule has updated the desired count for the Amazon ECS service from eight running tasks to 10 running tasks. Therefore, Amazon ECS only stops two of the 12 healthy running tasks, which reduces the task count back down to its current desired count of 10 running tasks. Limited maximum percent You’re running a service with a desired count of eight tasks and because of downstream limits or infrastructure constraints you have set a maximum percent of 100%. This doesn’t allow Amazon ECS to launch any additional tasks in parallel with your eight running tasks. If a task from this deployment freezes, or becomes overburdened and starts failing health checks, then Amazon ECS needs to replace it. Amazon ECS stops the unhealthy task first, then launches a replacement task after the unhealthy task has been stopped. This means the running task count still temporarily dips below the desired count. Task fails health checks during a rolling deployment You’re running a service with a desired count of eight tasks and a maximum healthy percent of 150%. You’re doing a rolling deployment to update your running tasks to be based off of a new task definition. Because the maximum healthy percent is 150%, this allows Amazon ECS to launch additional tasks in parallel with your currently running tasks. The rolling deployment has already triggered four additional task launches. The service currently has 12 running tasks: eight old tasks and four new tasks. During this rolling deployment, some of the old tasks begin failing a health check due to an unexpected bug. Because there’s an active rolling deployment occurring, Amazon ECS resorts to terminating unhealthy tasks immediately and replacing them with instances of the new task as quickly as possible. During a rolling deployment, Amazon ECS always try to replace failing tasks with tasks from the new active deployment. Health checks and responsive absorption of workload spikes Previously, Amazon ECS always stopped unhealthy tasks first, then launched a replacement task. This behavior made sense in a world where tasks were binpacked densely onto a statically sized cluster of Amazon EC2 instances that had no room to launch a replacement task without stopping an existing task. But more modern container workloads are now running using serverless AWS Fargate capacity. There’s no need to stop an unhealthy running task to make room for its replacement, as AWS Fargate can supply as much on-demand container capacity as needed. Additionally, many customers of Amazon ECS on Amazon EC2 are now using Amazon ECS capacity providers to launch additional Amazon EC2 instances on demand, rather than deploying to statically sized clusters of Amazon EC2 instances. Therefore, Amazon ECS now prioritizes using the maximumPercent for a service, and whenever possible it keeps unhealthy tasks running until after their replacements have become healthy. Additionally, the new Amazon ECS task replacement behavior helps prevent runaway task termination. In some cases, a large workload spike could cause a few tasks from the deployment to become unhealthy, which triggered their replacement. However, when Amazon ECS stopped unhealthy tasks in order to launch a replacement, the load balancer would shift more workload onto the remaining healthy tasks, which caused them to go unhealthy. In quick succession, all healthy tasks would be overwhelmed with workload that caused a cascade of runaway health check failures until every task had gone unhealthy. Eventually, Application Auto Scaling rules would kick in and scale up the deployment to a large enough size to handle the workload. But in most cases, a traffic spike causes the load balancer health checks to fail before it triggers aggregate resource consumption-based autoscaling. Auto scaling rules need to observe at least one minute of high average resource utilization before they react by scaling out the container deployment. However, an overburdened task may begin failing load balancer health checks immediately. In the scenario where your tasks are unhealthy because they are dealing with a large spike of incoming workload, the new task replacement behavior of Amazon ECS dramatically improves availability and reliability of your service. Amazon ECS catches health check failures and proactively launches a parallel replacement task that can help absorb the incoming workload spike before autoscaling rules even trigger. Once autoscaling rules trigger, the replacement task and the original task are both retained, if they are both healthy and if they fulfill the current desired task count of the service. Conclusion In this post, we explained new Amazon ECS behavior when handling unhealthy tasks. As more customers adopt Amazon ECS for their mission critical applications, we are always happy to tackle challenging new orchestration problems at scale. This updated task replacement behavior is designed to help serve the needs of customers both small and large. It helps keep your container deployments online and available—even in adverse circumstances such as application failure or traffic spikes. Please visit the Amazon ECS public roadmap for more info on additional upcoming features for Amazon ECS or to create your own issue to request a change or new feature. For more info on Amazon ECS scheduler behavior, see the official documentation, under Service Scheduler Concepts. View the full article

How do you monitor a container workload running on ECS (Elastic Container Service) and Fargate with on-board resources? Here are the prioritized aspects when it comes to monitoring containers on AWS... Event-driven monitoring with EventBridge Monitoring entry points like ALB, SQS, and Kinesis Monitoring inter-service communication (Service Connect) Observing container utilization Collecting and analyzing container logs View the full article

In this post, we’ll explore how to publish and consume services running on Amazon Elastic Container Service (Amazon ECS) and AWS Lambda, as Amazon VPC Lattice services. For an introduction to Amazon VPC Lattice, please read the documentation here. One main reason customer experience a lower velocity of innovation, is the complexity they deal with while trying to ensure that their applications can communicate in a simple and secure way. Amazon VPC Lattice is a powerful application networking service that removes this complexity, and gives developers a simpler user experience to share their application and connect with dependencies without having to setup any of the underlying network connectivity across Amazon Virtual Private Clouds (Amazon VPCs), AWS accounts, and even overlapping IP addressing. It handles both application layer load balancing and network connectivity, so that developers can focus on their applications, instead of infrastructure... View the full article

Today, AWS announces the availability of AWS Fargate for Amazon ECS Windows containers in the AWS GovCloud (US) Regions. This feature simplifies the adoption of modern container technology for Amazon ECS customers by making it even easier to run their Windows containers on AWS. View the full article

AWS Cloud Map introduces a new API for retrieving the revision of your services. It allows your applications to update the state of your cloud resources only when it has changed, minimizing the discovery traffic and API cost. With AWS Cloud Map, you can define custom names for your application resources, such as Amazon Elastic Container Services (Amazon ECS) tasks, Amazon Elastic Compute Cloud (Amazon EC2) instances, Amazon DynamoDB tables, or other cloud resources. You can then use these custom names to discover the location and metadata of cloud resources from your applications using AWS SDK and authenticated API calls. View the full article

While AWS ECS and EKS serve a similar purpose, they have several fundamental differences. Here's what you should know. View the full article

Understand the benefits of using Kubernetes or AWS ECS. Understand how they're different, and find out which tool is best for your situation. MetricFire can answer your questions about both Kubernetes and AWS ECS. View the full article

When it comes to container orchestration tools for managing and scaling microservices, two of the biggest tools in the market are Kubernetes and Amazon Elastic Container Service (ECS). Choosing the right tool can have a significant impact on your application’s scalability, management, and overall operational efficiency. In this blog post, we will thoroughly review each tool individually, discussing its advantages and disadvantages. By the end of the comparison, you will have a clear understanding of which container orchestration tool, Kubernetes or Amazon ECS, is the most suitable choice for your web application based on your company’s specific needs. So, let’s dive into the details and evaluate these two popular options. Amazon ECS vs. Kubernetes: Ultimate Comparison In the world of container orchestration, Kubernetes and Amazon Elastic Container Service (ECS) are two prominent tools. Kubernetes, developed by Google and hosted in the cloud, is a widely adopted container orchestration service that leverages Docker. It boasts a robust community and ecosystem. On the other hand, Amazon ECS is a container orchestration tool that excels in scalability. It dynamically creates additional containers to meet application demand. Both tools have their own set of strengths and weaknesses, making it crucial to thoroughly review them in order to make an informed decision that aligns with your business requirements. Amazon Elastic Container Service (ECS) and Kubernetes are two prominent container orchestration platforms that offer powerful capabilities for managing containerized applications. While both solutions serve the purpose of container orchestration, they have distinct differences in terms of architecture, management philosophy, scalability, and ecosystem integration. In this comprehensive comparison, we will delve into the key aspects of Amazon ECS and Kubernetes to help you make an informed decision about which platform is better suited for your specific needs. Architecture ECS follows a simpler architecture, with a control plane managed by AWS. It uses a task definition to define the containerized application’s specifications and runs tasks on EC2 instances or AWS Fargate, a serverless computing engine. www.amazon.com Kubernetes employs a more complex architecture with a master control plane and worker nodes. It uses various components like the API server, scheduler, and controller to manage containers, services, and resources across a cluster of nodes. www.kubernetes.io Management Experience Amazon ECS provides a fully managed experience, where AWS handles the underlying infrastructure and manages the control plane. This simplifies the setup and management process, making it easier for users to focus on deploying and scaling applications. Kubernetes offers a flexible and customizable experience but requires more configuration and management effort. Users have more control over the environment but need to handle tasks like cluster setup, scaling, and upgrades themselves. Scalability and Flexibility The scalability of container orchestration platforms is a critical factor to consider when choosing the right tool for your needs. Both Kubernetes and Amazon ECS have made significant strides in scaling their deployments to accommodate larger clusters. With the release of Kubernetes version 1.6, the platform introduced the ability to scale up to 5,000 node clusters. This means that Kubernetes can effectively handle the management and orchestration of a vast number of nodes within a single cluster. Additionally, if the need arises to scale beyond this limit, Kubernetes supports the use of multiple clusters, allowing for further scalability. Similarly, Amazon ECS has demonstrated its scalability by successfully scaling up to over a thousand container nodes without noticeable performance degradation. This showcases its ability to handle large-scale deployments and accommodate the growth of containerized applications. ECS provides robust scaling capabilities, allowing users to scale their tasks or services automatically based on predefined rules or application demand. It integrates seamlessly with other AWS services, such as Auto Scaling, ELB, and CloudWatch, to achieve dynamic scaling. Meanwhile, Kubernetes offers extensive scaling features, including horizontal pod autoscaling and cluster autoscaling. It allows users to define custom scaling rules and can scale workloads across multiple clusters or even cloud providers. Ecosystem and Community Amazon ECS benefits from the extensive AWS ecosystem, including various complementary services like AWS Fargate, Amazon ECR for container registry, and integration with AWS IAM, CloudWatch, and CloudFormation. However, the ECS community is relatively smaller compared to Kubernetes. On the other hand, Kubernetes has a vast and thriving community, with a rich ecosystem of third-party tools, plugins, and integrations. It supports multiple container runtimes, cloud providers, and operating systems, providing more flexibility and choice. Learning Curve and Adoption The Amazon ECS offers a simpler learning curve, making it easier for users to get started quickly, especially if they are already familiar with AWS services. It is well-suited for organizations heavily invested in the AWS ecosystem. Kubernetes has a steeper learning curve, requiring users to understand its concepts, APIs, and YAML-based configurations. However, Kubernetes has gained widespread adoption and is considered a de facto standard for container orchestration, making it a valuable skill in the industry. Advantages of Kubernetes over Amazon ECS Here are some pros and cons of Kubernetes over Amazon ECS have been listed below: Deployment Flexibility: Kubernetes can be deployed on-premises, in private clouds, and public clouds, providing greater flexibility and avoiding vendor lock-in. It can run on any x86 server or even on laptops, enabling organizations to choose the deployment environment that best suits their needs. In contrast, Amazon ECS is limited to running containers on the Amazon platform. Wide Variety of Storage Options: Kubernetes supports a wide range of storage options, including on-premises SANs and public cloud storage services. This flexibility allows organizations to utilize their existing storage infrastructure or leverage storage solutions from different providers. In contrast, Amazon ECS primarily relies on Amazon’s storage solutions, such as Amazon EBS, limiting the options for external storage. Extensive Experience from Google: Kubernetes is built on Google’s extensive experience in running Linux containers at scale. The platform inherits valuable insights and best practices from Google’s internal container management systems. This experience contributes to the robustness and reliability of Kubernetes, making it a trusted choice for organizations. Enterprise Offerings and Support: Kubernetes is backed by enterprise offerings from both Google (Google Kubernetes Engine – GKE) and RedHat (OpenShift). These offerings provide additional features, support, and services tailored for enterprise environments. They ensure that organizations have access to professional support and enterprise-grade capabilities when using Kubernetes. In comparison, Amazon ECS is validated and supported within the Amazon ecosystem and does not have as many options for enterprise-grade support outside of Amazon. Largest Community and Open Source: Kubernetes boasts the largest community among container orchestration tools, with over 50,000 commits and 1200 contributors. This vibrant community ensures a wealth of resources, including extensive documentation, tutorials, plugins, and third-party integrations. It also promotes rapid development and innovation within the platform. In contrast, while Amazon ECS has open-source components like Blox, the overall community and code contributions are smaller. Considering these advantages, Kubernetes offers greater deployment flexibility, a wider range of storage options, industry expertise from Google, extensive community support, and enterprise-grade offerings from multiple vendors. These factors make Kubernetes an attractive choice for organizations looking for a highly flexible and widely adopted container orchestration solution. Common features between ECS and Kubernetes The common features that exist between Amazon ECS and Kubernetes were listed below: Networking Both Kubernetes and Amazon ECS provide networking features such as load balancing and DNS. They enable applications to be accessed from the internet and distribute traffic among containers or instances. Overall, Kubernetes offers flexibility, multi-cloud support, a rich ecosystem, advanced scaling capabilities, and industry adoption, making it a powerful choice for container orchestration. Its ability to avoid vendor lock-in and provide granular control over workload scaling sets it apart from ECS and other container services, allowing organizations to leverage the most suitable platform for their evolving needs. Logging and Monitoring For Kubernetes, there are various external tools available for logging and monitoring, including Elasticsearch/Kibana (ELK), Heapster/Grafana/InfluxDB. These tools offer capabilities for collecting logs, analyzing performance metrics, and visualizing data. In the case of Amazon ECS, the partner ecosystem includes external tools such as Datadog and Sysdig Cloud, in addition to the built-in logging and monitoring services provided by AWS CloudWatch and CloudTrail. These tools offer similar functionalities for logging, monitoring, and analyzing containerized applications in the ECS environment. Autoscaling Both Kubernetes and Amazon ECS support native autoscaling. This means that the container orchestration platforms can automatically scale the number of running instances or containers based on predefined metrics or rules. Autoscaling helps maintain application performance and efficiently utilize resources by adjusting the container or instance count as demand fluctuates. Management Tools Kubernetes management actions can be performed using the kubectl command-line interface (CLI) and the Kubernetes Dashboard, a web-based user interface. These tools allow users to manage and control various aspects of their Kubernetes clusters and applications. In the case of Amazon ECS, management can be done through the AWS Management Console, which provides a graphical user interface (GUI) for managing ECS resources, configuring services, and monitoring containers. Additionally, the AWS Command Line Interface (CLI) offers a command-line tool for interacting with ECS and performing management tasks. Both Kubernetes and Amazon ECS offer networking capabilities, logging, and monitoring options, support for autoscaling, and management tools. However, the specific tools and services may differ, and users can choose based on their preferences and requirements. FAQs Why is Kubernetes superior to ECS? If you have complete and detailed control over whether your workload can scale using Kubernetes. When you need to transition to a more powerful platform, you may prevent vendor lock-in with ECS or any of the other container services by doing this. Is Kubernetes similar to Amazon ECS? Amazon ECS is comparable to EKS, except instead of using Kubernetes, it uses a proprietary control plane. The hosting infrastructure must be provisioned by the user, but ECS manages container orchestration. What distinguishes ECS and EKS most significantly? Elastic Kubernetes Service (AWS EKS) is a completely managed Kubernetes service, whereas Elastic Container Service (AWS ECS) is a fully managed container orchestration service. This is the main distinction between AWS EKS and AWS ECS. Whether Amazon ECS is scalable? AWS’s ECS is a fully-managed, highly scalable container orchestration solution. It makes running, stopping, and managing Docker containers on a cluster simple. For individuals who already use AWS and are looking for an easy way to run and grow containerized apps, the service is a popular option. Amazon Elastic Container Service is primarily used for? Amazon Elastic Container Service (ECS) is primarily used for container orchestration and management. It allows you to run and manage Docker containers in a highly scalable and reliable manner. Conclusion After closely examining the features and characteristics of Kubernetes and Amazon ECS, it is time to determine which container orchestration tool is the best fit for your needs. If you require multi-cloud capabilities and want the flexibility to deploy your applications across various cloud providers, Kubernetes emerges as the clear choice. Its extensive community support, rich ecosystem, and ability to work with multiple container runtimes make it an ideal option for organizations seeking a multi-cloud strategy. On the other hand, if your primary focus is on reducing IT labor, hosting costs, and management complexity, Amazon ECS is the recommended choice. Its fully managed nature and seamless integration with other AWS services simplify the deployment and scaling processes, allowing you to focus more on your applications rather than infrastructure management. Ultimately, the decision between Kubernetes and Amazon ECS depends on your specific requirements and priorities. To learn more about Amazon ECS and Kubernetes, try our hands-on labs and sandboxes. If you have any questions about this blog post, please feel free to comment us! View the full article

We are pleased to announce that HashiCorp Consul on Amazon Elastic Container Service (ECS) 0.5 is now generally available. This release adds support for authenticating services and clients using AWS Identity and Access Management (IAM) identities. The new release also adds support for mesh gateways, which enable services to communicate across multiple runtimes and clouds and reduces risk for organizations by enforcing consistent end-to-end security for service communication. View the full article

Amazon ECS now fully supports multiline logging powered by AWS for Fluent Bit for both AWS Fargate and Amazon EC2. AWS Fluent Bit is an AWS distribution of the open-source project Fluent Bit, a fast and a lightweight log forwarder. Amazon ECS users can use this feature to re-combine partial log messages produced by your containerized applications running on AWS Fargate or Amazon EC2 into a single message for easier troubleshooting and analytics. View the full article

Today, we are announcing availability of a Bottlerocket variant that supports NVIDIA GPU-based Amazon EC2 instance types on Amazon Elastic Container Services (Amazon ECS). Bottlerocket is a Linux-based operating system that is purpose-built to run container workloads. Customers can now benefit from using the same container-focused host operating system for both their non-GPU and GPU workloads while using ECS, including machine learning, video encoding, and streaming workloads. This helps customers standardize on a single operating system that utilizes the underlying specialized compute hardware. View the full article

Amazon Elastic Container Services (Amazon ECS) provides a Cluster Auto Scaling (CAS) capability to dynamically manage the scaling of your Amazon Elastic Compute Cloud (EC2) Auto Scaling groups (ASG) on your behalf, so that you can focus on running your containers. Capacity Providers is the compute interface that links your Amazon ECS cluster with your ASG. 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 ECS. Starting today, we are simplifying the integration mechanism between Capacity Providers and ASGs by directly integrating with target-tracking scaling policy instead of relying on AWS Auto Scaling scaling plan. View the full article

AWS Resilience Hub now supports Amazon Elastic Container Service (Amazon ECS), Amazon Route 53, AWS Elastic Disaster Recovery, AWS Backup, and the ability to use Terraform as a source to upload applications. With this expansion of supported resources, you can use Resilience Hub to prepare and protect even more of your applications from disruptions. View the full article

Thanks to Marc Weaver at Databasable, who helped us curate few interesting observations he made while working with these services.References:https://aws.amazon.com/eks/pricing/ https://aws.amazon.com/ecs/pricing/ https://aws.amazon.com/fargate/faqs/ https://docs.aws.amazon.com/eks/latest/userguide/s.. View the full article

The Amazon Elastic Container Service (Amazon ECS) extensions module that extends the service construct in AWS Cloud Development Kit (AWS CDK), is now generally available. The new Amazon ECS service construct for AWS CDK supports extensions that automatically add additional capabilities such as AWS App Mesh or FireLens to your containerized services using familiar programming languages. View the full article

Amazon Elastic Container Service (Amazon ECS) Cluster Auto Scaling (CAS) now offers more responsive scaling when using EC2 Auto Scaling groups (ASGs) that span across Availability Zones (AZs) and instance types. View the full article

Amazon Elastic Container Service (ECS) now supports the ability to add the recently launched P4d instances on Amazon ECS clusters in all regions where P4d instances are available. P4d instances offer up to 60% lower cost to train compared to previous generation instances with 2.5X more deep learning performance using the latest NVIDIA A100 Tensor Core GPUs. These instances also offer 8 TB of local NVMe storage. P4d instances are currently available in the US East (N. Virginia) and US West (Oregon) regions. To learn more about P4d instances, please visit the P4d product page and news blog. View the full article

Amazon Elastic Container Service (Amazon ECS) on AWS Fargate now lets you configure the size of ephemeral storage for your Tasks up to a maximum of 200GiB. All ephemeral storage on AWS Fargate continues to be encrypted by default with service-managed keys. View the full article

Today, Amazon Elastic Container Service (Amazon ECS) announced in preview, Amazon ECS deployment circuit breaker, for EC2 and Fargate launch types. With this feature, Amazon ECS customers can now automatically roll back unhealthy service deployments without the need for manual intervention. This empowers customers to quickly discover failed deployments, without worrying about resources being consumed for failing tasks, or indefinite deployment delays. View the full article

AWS App2Container (A2C) is a command-line tool for modernizing .NET and Java applications into containerized applications. A2C analyzes and builds an inventory of all applications running in virtual machines, on-premises or in the cloud. You simply select the application you want to containerize, and A2C packages the application artifact and identified dependencies into container images, configures the network ports, and generates the ECS task and Kubernetes pod definitions. View the full article

Amazon Elastic Container Service (Amazon ECS) now supports the use of Amazon FSx for Windows File Server in Amazon ECS task definitions. With this capability, you can now use persistent, shared storage across ECS containers. Customers can use Amazon FSx for their Windows containers in task definitions compatible with the EC2 launch type. Amazon ECS tasks using Amazon FSx will automatically mount the file systems specified by the customer in the task definition and make them available to the containers in the task across all availability zones in an AWS Region. View the full article

Amazon Elastic Container Service (ECS) now supports native Internet Protocol version 6 (IPv6) for Amazon ECS tasks using task networking (awsvpc networking mode). Previously, IPv6 was only supported in host networking mode. With this capability, tasks using awsvpc networking mode can communicate with other endpoints in Amazon Virtual Private Cloud (Amazon VPC) and internet in dual-stack mode via either IPv4 or IPv6. This will allow customers to communicate with on-premises resources that support only IPv6 addresses and meet IPv6 compliance requirements. View the full article

AWS Fargate for Amazon Elastic Container Service (Amazon ECS) announced features to improve configuration and metrics of containers: environment files, secret versions and JSON keys, granular network metrics, and more metadata. View the full article

Amazon Elastic Container Service (Amazon ECS) on AWS Fargate capacity providers is now supported in AWS CloudFormation, which makes it easier to manage and run Amazon ECS tasks across Fargate and Fargate Spot. You can now use CloudFormation to automate the management of Fargate capacity providers, associate them with ECS clusters, and specify capacity provider strategies at the cluster and service level by using a CloudFormation template. View the full article