Enhancing Open Journal Systems Through DevOps Practices

By Rahul Guhathakurta

The increasing demand for digital publishing and the rise of open-access journals have made effective management of journal platforms more crucial than ever. One such solution is the Open Journal Systems (OJS), a widely used open-source software for hosting scholarly journals, which has become a vital tool for many academic publishers. However, managing and maintaining OJS presents challenges, such as ensuring system stability, maintaining software quality, and deploying updates efficiently—all while minimizing downtime. This is where DevOps practices come into play, offering a transformative approach to streamline these processes and maximize efficiency.
By integrating DevOps methodologies into the management of OJS, journal administrators and developers can create a more reliable and scalable publishing environment. DevOps practices such as continuous integration/continuous deployment (CI/CD), automated testing, infrastructure as code, containerization, and proactive monitoring can significantly enhance the workflow. These techniques improve the software's quality and reliability and empower the team to deploy updates more frequently and confidently. In this article, we will explore how the implementation of DevOps practices can elevate the efficiency and reliability of OJS, transforming it into a resilient and agile platform for scholarly publishing.

Continuous Integration/Continuous Deployment (CI/CD)

In the context of OJS, CI/CD allows developers to automate code testing, building, and deployment, ensuring that all changes are verified consistently before reaching production. Using CI/CD tools like Jenkins, GitLab CI/CD, or GitHub Actions, developers can set up workflows that automatically build and test new code as soon as it is committed. This approach minimizes manual intervention, reduces human error, and accelerates the software release processes.
With automated pipelines in place, bug fixes, feature enhancements, and security updates are thoroughly tested before they are deployed to production. These pipelines can include multiple stages: code compilation, static analysis, unit tests, integration tests, and acceptance tests. This comprehensive process identifies issues early in the development cycle, enabling higher reliability and quality. Moreover, automated CI/CD workflows allow developers to implement a continuous feedback loop, where potential issues are addressed in near real-time, reducing the risk of significant disruptions.
The seamless deployment of OJS updates through CI/CD also means minimal user downtime. Traditional deployment methods often involve manual steps that can lead to extended periods of unavailability for end-users—such as editors, authors, and reviewers, especially during critical updates. Using a CI/CD approach, deployments become predictable and consistent, often utilizing strategies like blue-green deployments or rolling updates to maintain system availability. This way, end-users can continue to access the journal platform without interruption, thereby improving user satisfaction and maintaining the credibility of the publishing platform.

Automated Testing

Developers can conduct unit, integration, and acceptance tests using automated testing tools on new code before OJS is pushed to production. These tests help validate that individual components function correctly, verify how different system parts interact, and ensure that new features meet user requirements. Potential issues can be identified and resolved by thoroughly testing each aspect of the software, including the themes and third-party plugins.
This early detection of issues is vital for maintaining system stability and ensuring the overall quality of the software. Automated testing reduces the chances of bugs reaching production, thus preventing unexpected system failures that could disrupt journal operations. These tools also speed up the testing process compared to manual methods, allowing developers to quickly iterate on improvements and release new features more confidently, ultimately delivering a more stable user experience (UX).

Infrastructure as Code (IaC)

Infrastructure setup and configuration can be automated using tools like Terraform or Ansible, ensuring that OJS environments are consistent across development, testing, and production stages. Instead of manually configuring servers, IaC allows developers to define the infrastructure as code, reducing the potential for human error and making it easier to replicate and maintain environments. For OJS, this means smoother deployments, with each climate matching the desired specifications, leading to fewer surprises during production.
Another advantage of IaC is the ability to version and track changes in the infrastructure setup. As software code is managed using version control systems, IaC allows infrastructure configurations to be versioned and stored in repositories like Git. This makes it possible to track modifications, revert to previous configurations, or even roll back changes if needed. For OJS, where updates, plugins, and dependencies may need specific infrastructure settings, having version-controlled infrastructure provides a layer of security and transparency. It ensures that any changes to server configurations are documented, traceable, and can be replicated when needed, which is especially useful for troubleshooting or setting up new instances. In essence, version-controlled infrastructure ensures that the OJS platform remains secure, transparent, and reliable, thereby maintaining the credibility of the publishing platform.
IaC also makes scaling infrastructure based on demand much more straightforward. With the ability to define resources programmatically, provisioning additional servers or adjusting capacity to meet increased demand can be done with minimal effort. For OJS, this is especially beneficial during periods of high activity, such as special journal issues or conferences when traffic spikes are expected. Using IaC tools, administrators can scale the infrastructure dynamically, adding more resources or balancing the load without manual intervention. This flexibility leads to a more responsive system that can adapt quickly to changing needs, enhancing the overall user experience for authors, reviewers, and readers.

Containerization

Containerization tools like Docker and OJS and all their dependencies—such as web servers, database configurations, and application-specific libraries—can be bundled into standardized containers. This approach guarantees that the software environment remains identical across the development, staging, and production stages. As a result, developers can avoid the typical "it works on my machine" issue since every container includes everything needed to run OJS, irrespective of where it is deployed. This consistency is crucial for maintaining stability and efficiency in the software lifecycle.
In addition to creating consistent environments, tools like Docker Compose and Kubernetes facilitate the management and orchestration of multiple containers, simplifying deployment and ensuring easy scalability (which we will be discussing below as a separate point). Docker Compose can define and run multi-container OJS environments, handling services like the web server, database, and additional plugins. At the same time, Kubernetes takes scalability to a higher level by managing clusters of containers. Using Kubernetes, OJS deployments can be easily scaled to accommodate increasing workloads, automatically managing resource allocation and balancing load across multiple servers. This orchestration capability enhances deployment efficiency and ensures that the OJS platform remains robust and responsive, providing a seamless experience for all users.

Monitoring and Logging

By utilizing monitoring tools such as Prometheus, Grafana, or the ELK Stack (Elasticsearch, Logstash, Kibana), teams can keep a close watch on the performance of OJS, track user activity, and manage error logs effectively. These tools collect and visualize data in real-time, providing crucial insights into how the system performs, which resources are used, and where potential bottlenecks might be forming. Prometheus, for instance, can alert people on predefined thresholds. At the same time, Grafana visualizes data in intuitive dashboards that help teams understand the overall state of the OJS environment at a glance.
These monitoring and logging solutions are also pivotal in proactive issue detection and resolution. By continuously analyzing system health and identifying anomalies, teams can catch potential problems before they escalate into severe outages or performance degradation. For example, the ELK Stack enables detailed analysis of error logs, which can help developers trace and address issues swiftly. In the context of OJS, where uninterrupted access is critical for editors, authors, and readers, proactive monitoring helps maintain high availability and minimize disruptions. This approach ensures a more stable platform and enhances user trust by providing a reliable service.

Collaboration and version control 

Collaboration and version control are fundamental aspects of managing the OJS codebase effectively. Tools like Git facilitate seamless collaboration among team members, enabling developers to make modifications without disrupting the main codebase. Using branching strategies, team members can work on new features, bug fixes, or experimental changes in isolated environments. This process allows for comprehensive review and testing through pull requests, ensuring that changes are scrutinized and validated before merging into the main branch. This structured approach minimizes the risk of introducing errors and promotes higher code quality, making it easier for teams to implement enhancements or resolve issues within OJS.
Moreover, adopting a "shift-left" strategy encourages a collaborative culture between development and operations from the project's inception. By involving operations teams early in the development process, potential deployment challenges can be identified and addressed proactively. This close collaboration fosters better team communication and understanding, leading to more efficient workflows and smoother deployment processes. In the context of OJS, this approach not only streamlines the transition from development to production but also enhances the overall stability of the platform. Ultimately, combining version control systems and collaborative practices creates a more resilient development environment that supports continuous improvement and innovation.

Configuration management 

Tools such as Puppet, Chef, and Ansible provide robust solutions for automating and managing configurations across various environments. By employing these tools, teams can ensure that settings for OJS are consistently applied, whether in development, testing, or production. This consistency is vital for avoiding discrepancies that could lead to operational issues or unexpected behavior when moving code between different stages of deployment. Moreover, these tools allow teams to define configurations as code, making it easier to version, track changes, and roll back to previous settings when necessary.
Configuration management becomes critical when dealing with complex setups involving multiple plugins, database connections, and server configurations. OJS environments often require specific settings for these elements to function correctly, and any deviation can lead to significant issues. Configuration management tools help automate the deployment and configuration of these intricate setups, reducing the risk of human error and streamlining the onboarding process for new team members. This systematic approach enhances reliability and accelerates the deployment of new features and updates. Ultimately, by leveraging configuration management, teams can maintain a stable and efficient OJS platform that meets the evolving needs of users while minimizing downtime and operational disruptions.

Scalability and High Availability

DevOps methodologies-driven teams can automate the scaling of OJS infrastructure, mainly when deployed on cloud platforms. This automation is achieved through load balancers and auto-scaling groups, which dynamically allocate resources based on real-time traffic patterns. Kubernetes complements this with tools like the Horizontal Pod Autoscaler, which modifies the number of pod replicas based on CPU usage or other custom metrics. The load balancers efficiently distribute incoming user requests across multiple server instances, ensuring no single server becomes overwhelmed. At the same time, auto-scaling groups automatically adjust the number of active server instances based on the current load. This intelligent resource management keeps the system responsive and minimizes the risk of downtime, providing end-users with a seamless experience. Furthermore, the ability to scale resources up or down as needed optimizes operational costs and enhances the OJS platform's overall resilience, making it better equipped to adapt to the fluctuating demands of the academic publishing landscape.
About the Author:
Rahul Guhathakurta (ORCID: 0000-0002-6400-6423) is a strategic management consultant and a primary investor in IndraStra Global Publishing Solutions Inc. — a US-based publishing company.

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