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Sensible placement of workloads is important, especially for data-intensive applications performing real-time analysis using artificial intelligence (AI). These workloads require computing that happens on the edge, near where data is collected. However, this manner of distributed computing implies many servers in many remote environments that may not have on-site IT staff, or indeed any on-site staff at all. Those edge servers have to be managed remotely, and at high scale. Red Hat can help you extend your hybrid cloud management strategy to edge environments.

Remote server management

Data processing and storage have often been centralized in big data centers, offering efficiency and cost savings. Cloud computing initially promised that this would be the universal way that computing would be done. But things changed. Smart devices like smartphones and factory sensors create huge volumes of data and demand quick, on-site response. We're shifting towards processing data right at its source using small yet intelligent AI models, in what's known as the network's "edge".

Gartner predicts that by 2025, about 75% of enterprise data will be processed at the edge. By extending hybrid cloud strategies to the edge, your business can enjoy the scalability and efficiency of a cloud while also capitalizing on the speed and responsiveness of edge computing operating on local data. This blended approach offers a pathway for optimizing performance, scalability, and cost-effectiveness. This is especially important in sectors where localized speed is of the essence, such as manufacturing and telecommunications.

Overcome the challenges of edge computing

Transitioning to edge computing within a hybrid environment introduces unique challenges. Deploying edge servers across numerous sites is notably more complex than scaling up a central data center. The complexity of managing multiple locations, ranging from hundreds to hundreds of thousands of nodes and clusters, is compounded by the remote nature of many edge deployments, often with limited on-site IT support.

To effectively address this, your organization needs a centralized approach for deployment, management, and updates. With Red Hat's suite of products, your organization can achieve consistency, streamlined operations, while maintaining security across your distributed edge infrastructure:

  • Red Hat Enterprise Linux (RHEL) provides a reliable and flexible operating system that works in both a centralized datacenter and across edge locations.
  • Red Hat OpenShift and OpenShift AI offer a consistent application platform for unified management across different environments. OpenShift facilitates rapid application deployment and localized decision-making, even in areas with limited connectivity. Combined with Red Hat Quay for secured container registry and continuous analysis for vulnerabilities, it ensures the integrity and security of edge deployments.
  • Red Hat Ansible Automation Platform paired with Red Hat Satellite automates server management tasks. This combination ensures consistent deployment, management, and updates for edge devices and applications without manual intervention. It simplifies workload management and tackles operational complexities across edge sites, devices and clusters.
This diagram shows the business drivers and management between different environments


Red Hat's remote management in hybrid and edge

Red Hat products work together to deliver remote server management across a hybrid environment.

This is a schematic diagram showing the process of managing remote servers in a hybrid environment, detailing the flow between the core data center, cloud services and infrastructure management


Starting at the core data center, the Git-based Source Code Management (SCM) system supports collaborative development, version control, and centralized codebase management. It enhances efficiency and collaboration within your development pipeline.

The server image build pipeline, integrated with CI/CD workflow systems like Jenkins or GitLab CI, automates software compilation and packaging. This process guarantees consistent and reliable server image generation. Images are securely stored in an image registry, such as Red Hat Quay, which also facilitates vulnerability detection within container images.

Moving on to the hybrid cloud infrastructure, Red Hat OpenShift serves as the Kubernetes-based application development platform. Its emphasis on high performance and ease of use makes it well suited for deploying and managing containerized applications across diverse infrastructures, including edge environments. OpenShift capabilities, such as OpenShift AI, facilitate the deployment and management of AI workloads at scale, enabling real-time decision-making in edge scenarios. It allows for the efficient deployment and management of applications at scale, even in locations with minimal IT staff.

With RHEL, you're running an optimized operating system with a focus on enterprise needs and security. The Insights client  integrates with Red Hat Insights, providing real-time analysis of registered RHEL systems to identify potential issues before they impact operations.

Red Hat Ansible Automation Platform uses Ansible playbooks to automate complex workflows. This simplifies the management of applications and infrastructure across a variety of environments.

The Insights platform provides advanced analytics for operations, potentially integrating AI and machine learning models to forecast and mitigate operational risks.

Finally, for infrastructure management is where smart management tools like Satellite provide patch management, software distribution and standardized building of RHEL systems across different environments.

Remote server management architecture

Regardless of your industry, Red Hat can help you manage the edge. For a custom industrial edge solution designed for manufacturing environments, refer to the Industrial Edge architecture. This framework optimizes manufacturing processes by delivering real-time insights and harnessing AI/ML capabilities at the network edge. It improves efficiency, quality, and security while enabling smooth integration between core data centers and factory floors.

Similarly, the Enabling Medical Imaging Diagnostics with Edge architecture addresses medical diagnosis needs, especially in skin cancer detection, through edge computing. This architecture employs AI/ML algorithms for image analysis, facilitating real-time image processing at diagnostic facilities and model training at medical data centers. Such an approach can significantly enhance diagnostic efficiency and accuracy in medical imaging diagnostics.

Red Hat's approach to remote management in hybrid and edge computing environments uses the capabilities of a core data center to remotely manage edge computing environments at scale. This enables businesses to navigate the complexities of modern infrastructure management smoothly and efficiently.  To find out more, visit our Remote Server Management architecture overview.

About the author

Ruby started as a Product Marketing Manager on the Red Hat Architecture team in 2022. She focuses on creating solution-driven content and bridging customer challenges with Red Hat's innovative open source solutions.

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