How to Use Jenkins Effectively with ECS/EKS Cluster
Practical guidance for scaling Jenkins masters and agents across AWS container services.
Published by Bibin Skaria · Apr 2025
Introduction
In modern DevOps workflows, Jenkins is the cornerstone for Continuous Integration and Continuous Deployment pipelines. Because of its flexibility and wide-ranging plugins available, it's indispensable in the automation of build, test, and deployment processes. On the other hand, AWS provides Elastic Container Service and Elastic Kubernetes Service as powerful managed services for deploying and managing containerized applications. This article explores how Jenkins can be effectively integrated with both ECS and EKS clusters to optimize the CI/CD process.
Why Use Jenkins with ECS/EKS Clusters?
Scalability
One of the advantages of using AWS ECS and EKS for Jenkins deployment is its dynamic scaling. They allow Jenkins to handle CI/CD workloads that are variably required. For example, in case the build queue goes up, it can provide additional agents on its own as such demand dictates. That ensures at no time, especially during peak conditions-for instance, when huge projects or a lot of builds are triggered together,pipeline execution will result in hiccups. The ability to scale up and down based on workload needs ensures that Jenkins can handle both small-scale tasks and large, resource-intensive operations without interruption, making the system highly efficient and reliable in fluctuating environments.
Flexibility
Both ECS and EKS provide a great deal of flexibility in terms of resource allocation. In the case of both these services, Jenkins can dynamically provision agents based on demand, hence utilizing only those resources which are actually required at any given time. On ECS, agents can be deployed inside Fargate tasks, and on EKS, they can be deployed inside Kubernetes pods. That is dynamic provisioning in Jenkins: it will allocate exactly when required and deallocate as soon as the resources are not in use, therefore optimizing the overall infrastructure. Waste reduction due to on-demand scaling will keep Jenkins running efficiently so as to scale up fast into pipeline demands and keep costs under control.
Cost Efficiency
This is a major benefit one gets from using Jenkins with ECS, especially Fargate. The Fargate is a serverless compute engine that allows users to run containers without managing the infrastructures. Sometimes, in conventional environments, managing and scaling infrastructure manually requires much resource usage and is pretty expensive. However, with Fargate, the user pays for only what is consumed. Thus, Fargate is a pay-as-you-go model that finds its greatest usefulness in teams where the workload fluctuates. This is a perfect model for teams operating in an environment where flexibility and scalability are needed without manual continuous interference; hence, it's cost-effective for dynamic, high-performance CI/CD environments.
AWS ECS and EKS are optimum for Jenkins deployment because of the scalability and flexibility they offer. Dynamically scaling the workload demand ensures smooth execution during peak times with optimization in resource utilization. This allows teams to drastically reduce their operation costs and improve overall infrastructure efficiency by leveraging on-demand agent provisions and the pay-as-you-go model of Fargate. These benefits will make ECS and EKS a robust solution to maintain high-performance, cost-effective Jenkins pipelines as development environments keep fluctuating with dynamic workloads.
Architecture Overview
Infrastructure Components
A Jenkins deployment on AWS relies on several key infrastructure components that work together to create an efficient, scalable, and reliable CI/CD pipeline. Below is an in-depth breakdown of each component that plays a vital role in the architecture.
Jenkins Master
The Jenkins Master is the central control unit of the Jenkins deployment. It is responsible for orchestrating the entire build process, managing the job queue, and scheduling the execution of tasks. In a containerized setup, the Jenkins Master runs within a container, typically deployed on AWS ECS or EKS. This containerized deployment ensures that Jenkins is scalable, isolated from other processes, and can be easily managed. The Jenkins Master also manages communication with Jenkins agents, dispatching them tasks for execution. The containerized nature of the master allows for easy updates, scaling, and management as it is isolated from the underlying infrastructure.
Jenkins Agents
Jenkins Agents are the worker nodes responsible for executing the build and test tasks. They are provisioned dynamically, based on the workload, and can be scaled up or down depending on the build queue. For example, when there is a high demand for builds, Jenkins will automatically spin up new agents to ensure timely execution. Conversely, when the demand decreases, agents are terminated, allowing resources to be freed. In a cloud-based deployment using AWS ECS or EKS, Jenkins agents are containerized and can be run as ECS tasks or Kubernetes pods. This dynamic provisioning of agents allows for efficient resource usage, ensuring that Jenkins always has the necessary compute power for any given workload.
Persistent Storage
Persistent storage is crucial for maintaining the Jenkins state, logs, build artifacts, and configuration data. Since Jenkins needs to retain historical build data and logs, it's essential to use a reliable and scalable storage solution. AWS Elastic File System (EFS) and Amazon S3 are commonly used to provide this persistence. AWS EFS is a scalable, shared file storage service that can be accessed by multiple instances, making it ideal for Jenkins master and agents that require shared access to files and artifacts. On the other hand, Amazon S3 is used to store static files, including logs, build artifacts, and backups. Both EFS and S3 ensure data integrity and availability, even during scaling operations or node failures.
Monitoring
To ensure that the Jenkins deployment is running smoothly, it is crucial to have robust monitoring in place. AWS CloudWatch is a powerful tool that allows for the aggregation of logs and tracking the real-time performance of Jenkins. CloudWatch can collect logs from Jenkins, including build logs, system logs, and agent activity, helping to identify issues and bottlenecks in the pipeline. Additionally, CloudWatch allows for performance metrics such as CPU usage, memory consumption, and network traffic to be monitored, which helps in proactive resource management. By setting up CloudWatch alarms, teams can be alerted when thresholds are exceeded, ensuring quick responses to potential issues. This level of visibility and monitoring ensures that Jenkins workflows remain efficient, reliable, and responsive to changes in workload.
Together, these infrastructure components form a robust and scalable Jenkins architecture on AWS. The containerized Jenkins master, dynamic agent provisioning, persistent storage solutions, and integrated monitoring with CloudWatch all work in unison to create an efficient CI/CD pipeline capable of scaling with demand while maintaining high performance, reliability, and cost efficiency. This architecture makes Jenkins on AWS a powerful solution for modern DevOps workflows, where flexibility and automation are key to successful software delivery.
Comparing ECS and EKS for Jenkins Deployment
Choosing an appropriate container orchestration platform for deploying Jenkins will bring huge differences in the efficiency and management of your workflow. This comparison highlights the strengths of AWS ECS and EKS to help you decide which platform aligns best with your deployment needs.
If comparing ECS with EKS to deploy Jenkins, the first one would work for smaller systems, whereas, if combined with Fargate, it even offers the possibility to do serverless deployment without any kind of infrastructure to manage. While EKS gives a more controlling perspective through its Kubernetes-based orchestration and thus could fit better in case of complex workflows or multi-environment deployments within continuous integration or deployment.
Setting Up Jenkins on ECS
Step 1: Infrastructure Setup
Set up the infrastructure that would be necessary to set up Jenkins on ECS. Now, create an ECS cluster with appropriate networking configurations: VPCs, subnets, security groups. Next, define task definitions with the specification of container configuration and IAM roles required by your deployment.
Step 2: Deploy Jenkins Master
Once the infrastructure is available, containerize Jenkins and deploy it as an ECS service using Fargate; at this stage, create some task definitions for the Jenkins master, which will define configuration for container images, CPU and memory, but also IAM roles which will be applied to grant the required permissions to Jenkins.
Step 3: Dynamic Agent Provisioning
With regard to resource optimization, dynamic building agents' provisioning can be provisioned using the Jenkins ECS plugin. It manages the ECS tasks as Jenkins agents, and based on this logic, the agents would spin only when needed, automatically terminating at the end of the task to make the entire process smoother.
The following walkthrough provides detailed steps for setting up Jenkins on ECS, using AWS services like Fargate, and the Jenkins ECS plugin to simplify your continuous integration/continuous deployment pipelines. You will have a scalable setup with lesser infrastructure management and better resource utilization; hence, this is going to be a pretty robust solution for modern development workflows.
Deploying Jenkins on EKS
Step 1: EKS Cluster Configuration
To deploy Jenkins on EKS, begin by setting up an EKS cluster with essential configurations. Create Kubernetes namespaces and define RBAC policies to manage access and permissions effectively. Additionally, configure networking settings to ensure secure communication within the cluster and with external resources.
Step 2: Jenkins Deployment
To install Jenkins on Kubernetes, use Helm charts, which will make the process quite easy with predefined templates. The preconstructed templates allow for the easy creation of Jenkins master and agent pods, along with Persistent Volume Claims storing Jenkins data. Because of its modularity and ease of use, Helm is an excellent choice for deploying Jenkins in a Kubernetes environment.
Step 3: Persistent Storage and Logging
Store Jenkins itself using the AWS Elastic Block Store or Amazon S3. Make sure data is present there for persisting and is watched out for efficiency. For the logs, one can set up AWS CloudWatch log collection and visualization from Jenkins to enable debugging-easier, and thereby monitor effectively-continuous integration/continuous deployment workflows.
It gives the ability to leverage Kubernetes for scale and resilience in your continuous integration/continuous deployment by running Jenkins on EKS. You have full control over orchestration, easy integrations with any AWS service for storage and monitoring purposes, and a flexible platform for managing complex deployment scenarios. Properly configured and with the right tools, such as Helm, you will be assured of a reliable and efficient Jenkins environment that is tuned for a developer's needs.
Best Practices for Jenkins on ECS/EKS
Optimization of a Jenkins deployment on AWS will involve implementing resource efficiency, security enhancement, and robust monitoring and debugging. With this fine-tuning, you are able to create a resilient and cost-effective continuous integration/continuous deployment environment that supports your development workflows effectively.
Optimizing Resource UsageFor optimal resource utilization, enable auto-scaling policies on ECS and EKS agents to scale up and down based on the workload. Enable Fargate Spot instances so that agents provision in off-peak hours at a low cost to decrease operational costs without compromising performance.
Enhancing SecurityIncrease your Jenkins security, integrating role-based access control from your EKS setup with Secure resources to implement Jenkins-specific control in AWS Secret Manager in respect to secret keys storing encrypted confidential credentials or other data so important configuration could be kept confidentially.
Real-World Use Case
Cerebro platform was developed by Expedia Group to represent a huge leap in the company's managing databases at scale. Being a DBaaS platform, it makes rapid provisioning possible with efficient management of databases across all the infrastructures at Expedia. It is built for seamless integration with a wider technology ecosystem at Expedia, thus making it fast and consistent to manage databases at high bars of performance and security.
This contains one of the primary building blocks of Cerebro, which is extensive utilization of several AWS services to scale and meet varied requirements of Expedia. Amazon EC2 provides the scalable computer for Cerebro to make sure that the overall system can bear a wide array of workloads whenever necessary. For storage, Amazon DynamoDB is a NoSQL database fully managed, high performance, flexible for most of the workloads that require fast and consistent access to data. Besides that, Amazon Aurora is a relational database service that provides high performance to manage databases, including automated backups, scaling, and fault tolerance, hence very suitable for the transaction-intensive operations of Expedia.
AWS Glue also plays an important role in automating the workflows and processing data by allowing the ETL cycle of a data ingest process. That way, Expedia will be able to process large datasets and run analytics without setting up any complicated infrastructure. Additionally, Amazon ECS will orchestrate and manage containerized applications; hence, Expedia can easily run microservices and distributed applications.
Another major element is Amazon CloudWatch, which will enable the monitoring of databases, applications, and infrastructure performance in real time. It integrates very well with Cerebro to provide insight into the health of the platform and ascertains that any potential issues are identified and fixed quickly.
Cerebro was designed with governance and best practices in mind to help standardize database management across the full tech stack at Expedia. By forcing operational standards consistently, it ensures best practices for security, performance, and consistency of data, thereby improving overall reliability and performance of the platform.
These enable Expedia Group to reduce operational overhead and decrease the costs associated with managing databases. By adopting AWS' cloud technologies, operational flexibility in things such as rapidly scaling services during periods of high load created by peak travel seasons became much easier to implement. Such functionalities as dynamically provisioned resources, on-demand scalability for applications, and pay-only-for-what-you-use infrastructure have led to significant financial benefits. By finally harnessing the powerful infrastructure of AWS with Cerebro, Expedia is setting up for continued innovation and growth in one of the most competitive online travel industries, where speed and operating efficiency will determine the winners.
Challenges and Solutions
Slower Read/Write Operations with EFS
As you understand by now, AWS Elastic File System (EFS) provides scalable and reliable shared storage for Jenkins. The disadvantage however, compared to a local or block storage, is slower read/write operations, which causes performance problems when workflows require more access to storage. For mitigating this, the combination of EFS with Elastic Block Store (EBS) may be used. For highly IOPS-dependent build processes, perform storage-intensive operation of temporary build files and highly frequently accessed data on EBS for low latency access, and perform less time sensitive things, such as logs and backups, on EFS. The frequency of direct EFS access may be further reduced by implementing a caching mechanism using ElastiCache or the natively supported caching of artifacts by Jenkins itself for better performance.
Running Docker in Docker (DinD)
The traditional way of running DinD Jobs is hard to operate within a containerized environment. Jenkins controller/agent running as Docker containers needs access to the Docker socket on the host. That was less viable since Docker was getting deprecated as runtime in Kubernetes and also discouraged in the modern setup. The solution would be to use other tools instead of DinD, such as Kaniko, Buildah, or Podman, for the tasks that would normally require the DinD. These tools are designed in a containerized environment and, therefore, do not need a Docker runtime, hence working nicely with Kubernetes' CRI. In addition, they even provide additional security due to the reduced possibility to expose the Docker socket to the containerized environment.
Performance Bottlenecks
One of the common challenges in Jenkins deployments is performance bottlenecks, especially as workloads increase. A potential bottleneck can occur when the Jenkins master node is overwhelmed by high traffic or large numbers of concurrent builds. To mitigate this, you can use load balancing for Jenkins master nodes, distributing the load across multiple instances to ensure that no single node becomes a point of failure. Additionally, optimizing agent configurations is crucial to avoid resource exhaustion. This includes adjusting the allocated CPU, memory, and disk space for Jenkins agents to match the needs of the workloads they handle, as well as enabling dynamic provisioning of agents to spin up new instances when needed and scale down during idle periods.
Agent Management
Efficient agent management is critical for maintaining a smooth CI/CD pipeline. Using Jenkins plugins such as the ECS plugin for Amazon ECS or the Kubernetes plugin for EKS can streamline agent lifecycle management. These plugins automate the process of provisioning, scaling, and terminating Jenkins agents based on the workload. With the ECS plugin, for example, Jenkins can automatically launch ECS tasks as build agents and terminate them when no longer needed, optimizing resource usage and minimizing costs. Similarly, the Kubernetes plugin can manage agent pods dynamically, ensuring that only the necessary number of pods are running at any given time based on build queue demands.
Conclusion
In conclusion, integrating Jenkins with AWS ECS or EKS streamlines CI/CD workflows with scalable, flexible, and cost-efficient solutions. ECS allows easy deployment of Jenkins on Fargate, eliminating infrastructure management, while EKS provides Kubernetes-level control for complex setups. Moreover, benefits include dynamic scaling for fluctuating workloads, on-demand resource use to cut costs, and secure operations with features like role-based access and encrypted credentials. With AWS tools like CloudWatch for monitoring and EFS for storage, this setup ensures reliability and performance. To sum up, by adopting AWS-managed services, teams can build a robust, scalable Jenkins infrastructure to accelerate software delivery.