Through edge computing infrastructure, businesses are moving computation closer to where data is generated. This shift is associated with improved performance, security and responsiveness. As edge computing reshapes industries, IT leaders are tasked with building reliable, scalable and secure infrastructure that supports this evolution. 

In this article, we dive into what edge computing means, its essential components, benefits, challenges and best practices.

What is the edge?

Edge computing refers to the practice of processing data near its source instead of relying solely on centralized cloud data centers. The “edge” typically includes Internet of Things (IoT) devices, on-premises servers, local data centers and even base stations in telecom networks. By bringing compute, storage and analytics closer to users and devices, edge computing reduces latency, improves data sovereignty and enhances real-time decision-making capabilities.

This practice is critical for latency-sensitive applications such as autonomous vehicles, industrial automation and telemedicine. In these applications, milliseconds can make a difference in outcomes. The ability to process and act on data immediately is essential for safety, efficiency and user experience. 

Edge computing helps optimize bandwidth usage by minimizing the amount of raw data that must cross networks to centralized clouds. Edge computing also improves the performance of distributed applications by providing localized compute resources that can operate independently when network connectivity is limited or disrupted.

The essential components for edge computing infrastructure

Creating an effective edge computing infrastructure involves several key technology layers. Let’s break down the essentials:

1. Compute nodes: These are powerful yet compact servers or devices capable of handling processing tasks locally. They can be deployed across various locations, from retail stores to manufacturing floors. Compute nodes must be made rugged for harsh environments and scalable to meet growing demands.
2. Storage solutions: Reliable storage infrastructure ensures that critical data can be collected, stored and accessed without delays. Edge storage must handle high volumes of real-time data with built-in redundancy. Advanced storage solutions often include solid-state drives (SSDs) for speed and reliability and data replication for added resilience.
3. Network connectivity: High-speed, low-latency connections are the lifeblood of edge computing. 5G networks, Wi-Fi 6 and wired connections all play roles in connecting edge devices to central clouds and other endpoints. Redundant network paths and intelligent routing are often necessary to achieve uninterrupted service.
4. Edge orchestration and management software: Platforms like SUSE Edge enable centralized management of dispersed edge environments. They handle provisioning, updates, security policies and resource optimization. These tools provide automation, load balancing and real-time visibility into edge operations.
5. Security frameworks: With data processed outside traditional firewalls, security must be embedded at every level. Device authentication, encryption, secure boot processes and AI-driven anomaly detection are essential. Regular vulnerability assessments and secure firmware updates are critical to maintaining trust.
6. Monitoring and observability tools: Real-time monitoring ensures the health and performance of distributed nodes. Tools that offer centralized dashboards, alerts and predictive analytics help IT teams stay proactive. Observability extends to application performance, user experience and security metrics. They enable faster root-cause analysis.
7. AI and machine learning capabilities: Deploying AI models at the edge enables real-time insights and automation. Inferencing at the edge supports use cases like predictive maintenance, intelligent traffic management and personalized customer experiences. Machine learning at the edge reduces dependency on cloud resources and drives faster decision-making.

What are the benefits of using edge computing?

Edge computing infrastructure has several benefits for enterprises in all industries, including:

• Lower latency: Processing data closer to the source reduces communication delays. This reduction in delays enables real-time applications and better user experiences. In industries like healthcare, finance and autonomous transportation, even microseconds can make a significant impact on outcomes.

• Bandwidth optimization: By filtering and analyzing data locally, only essential information is sent to centralized clouds. This selective data transmission conserves bandwidth and minimizes operational expenses for businesses managing large IoT ecosystems.

• Enhanced data security and compliance: Keeping sensitive data on-site or within localized networks enhances privacy, data sovereignty and regulatory compliance. Edge computing supports compliance with regional laws such as GDPR by giving you control over where and how data is stored and processed.

• Increased reliability and resilience: Edge nodes can continue operating independently even if the cloud connection is disrupted. This resilience is especially valuable for critical infrastructure sectors like utilities, healthcare and emergency services where downtime can have severe consequences.

• Real-time insights: Enabling AI at the edge gives you more immediate insights, which are vital for industries like manufacturing, healthcare and smart cities. Real-time data analytics at the edge enables businesses to identify patterns, predict maintenance needs, optimize workflows and personalize customer experiences more effectively than centralized models.

• Greater scalability: Edge computing allows organizations to scale incrementally by adding new edge nodes as needed, rather than investing heavily in expanding central data centers. This flexible, modular approach supports faster time-to-market for new services and applications.

• Environmental benefits: Reducing the need to transfer large volumes of data back and forth between devices and the cloud can lead to lower energy consumption. Edge computing supports more sustainable IT operations by minimizing unnecessary data traffic and optimizing resource usage at the local level.

How edge computing redefines infrastructure

Edge computing fundamentally reshapes how we manage, process and act on data. Instead of sending everything back to a central data center, enterprises are pushing computing power closer to where the action happens. Why? Because speed matters. So does resilience. 

From connected factories to retail checkout systems, edge infrastructure is powering real-time decision-making, automation and customer experiences. Here’s how it’s transforming enterprise architecture:

• Distributed computing models: With edge computing, infrastructure is no longer centralized. Enterprises must manage thousands of distributed nodes efficiently and securely. With this decentralization comes greater resilience. It also enables real-time data processing at the source, driving faster decision-making and better customer experiences.

• Convergence of IT and OT: Operational technology (OT) like sensors and controllers is increasingly integrated with IT systems. This requires unified management platforms. The convergence empowers organizations to create smarter, more automated environments by combining operational insights with IT capabilities. Ultimately, it can improve business agility and operational efficiency.

• Micro data centers: Compact, modular data centers deployed at the edge enable rapid scalability without the need for massive on-premises construction. These micro facilities bring critical compute and storage capabilities closer to end-users. They support a new generation of decentralized applications in industries such as healthcare, manufacturing and retail.

• Software-defined everything: Network, storage, and compute resources at the edge are increasingly software-defined for agility, scalability and remote management. Software-defined infrastructure reduces the need for manual intervention. This allows organizations to dynamically allocate resources based on real-time needs and emerging business demands.

• Autonomous operations: AI-powered orchestration reduces the need for manual intervention, enabling self-healing, self-optimizing edge environments. Advanced analytics and machine learning models proactively identify and resolve issues. Doing so minimizes downtime and optimizes performance across distributed networks.

Challenges to implementing edge computing infrastructure

While the potential is significant, there are still challenges to deploying edge computing infrastructure:

1. Complex deployment and management: Coordinating thousands of geographically dispersed nodes adds complexity to deployment, updates and maintenance. Be sure to account for site-specific requirements, physical security, and local regulations while ensuring operational consistency across all locations.
2. Security vulnerabilities: Distributed environments increase the attack surface. For that reason, proactive security measures are more critical than ever. Each edge device becomes a potential entry point for cyber threats. They require endpoint protection, network segmentation and real-time threat detection to maintain a strong security posture.
3. Data integration: It is challenging to consolidate data from heterogeneous sources while maintaining consistency and quality. Edge deployments often involve diverse hardware, software and communication protocols. As such, flexible and interoperable data architectures are needed to deliver accurate and actionable insights.
4. Limited local resources: Unlike centralized cloud data centers, edge environments often face constraints in power, cooling and physical space. Designing efficient, compact, and energy-conscious infrastructure is essential to overcome these limitations and ensure reliable performance even in remote or harsh environments.
5. Standardization gaps: Edge computing is still evolving with inconsistent standards across industries and vendors. The lack of universal frameworks makes it difficult to integrate and manage multi-vendor environments. So leading organizations are prioritizing open standards and modular, vendor-neutral solutions to achieve long-term scalability. For example, Project Sylva and Project Margo are intended to standardize edge computing infrastructure in the telecom and manufacturing spaces, respectively.

Best practices to achieving edge computing success

Edge computing opens exciting doors, but it’s not without its hurdles. As more enterprises move workloads to the edge, they’re discovering that building and managing distributed infrastructure requires more than just technical savvy. It takes smart planning, cross-functional collaboration and an eye for long-term scalability. 

Here are the key obstacles to overcome in order to unlock the full potential of edge computing:

1. Start with a clear strategy: It’s easy to get caught up in the excitement of new technology. Real success starts with a clear plan. Define your business objectives first.  Then, identify the edge use cases that align most closely with your goals.
2. Invest in centralized orchestration: Managing thousands of distributed nodes isn’t easy. Platforms like SUSE Edge can help you streamline operations by centralizing security policies, updates and performance monitoring. This will make your edge environment easier to scale and secure.
3. Prioritize security from the ground up:  With so many potential points of entry, security can’t be an afterthought. Build on a zero-trust model and encrypt data both in transit and at rest. Always proactively monitor for vulnerabilities at every level.
4. Embrace open standards: Future-proof your architecture by choosing open-source, standards-based solutions. Open frameworks improve interoperability and protect you from vendor lock-in as the edge ecosystem evolves.
5. Build modular and scalable infrastructure: Edge computing needs to grow with your business. Design modular systems that can flexibly expand to handle increasing data volumes, new applications and shifting workloads.
6. Implement robust data governance: Data at the edge must be managed just as carefully as data in a centralized environment. Establish clear protocols for data ownership, compliance requirements and lifecycle management early to avoid complexity later. Strong governance makes scaling smoother and reduces risk across the board.

Edge computing infrastructure: Final thoughts

Edge computing infrastructure is essential for enterprises seeking agility, responsiveness, and innovation. It differs from traditional IT models in that it distributes computing closer to where data is generated, unlocking new possibilities across industries. 

By embracing the right strategies, security practices, and management tools, you can tap into the full power of edge computing to accelerate your organization’s digital transformation journey. From starting out to scaling an existing deployment, SUSE delivers the open, flexible and resilient technologies you need to succeed at the edge.

To learn more about edge infrastructure, download the Cloud Native Edge Essentials guide.

Edge computing infrastructure FAQs

What is an example of edge computing?
An example of edge computing is a smart factory where IoT sensors monitor equipment performance in real time. Instead of sending all sensor data to the cloud, local edge servers process the data instantly to detect anomalies and trigger predictive maintenance. This helps to prevent costly downtime. Another example are autonomous vehicles. They use edge computing to make split-second decisions based on sensor input without depending on remote data centers.

How much does edge computing infrastructure cost?

The cost of edge computing infrastructure depends on deployment size, complexity, hardware and management solutions. Small edge deployments could cost a few thousand dollars, while large industrial-scale rollouts can reach millions. Total cost considerations include hardware, software, networking, security and ongoing management. Additionally, enterprises should factor in future scalability needs. Growing data volumes and processing demands can significantly increase long-term costs.

Is edge computing infrastructure secure?

Yes, edge computing infrastructure can be highly secure when it is properly designed. Best practices include implementing zero trust architectures, encrypting data at all stages, securing endpoints and using continuous monitoring to quickly detect threats. It’s important to treat edge devices with the same security rigor as any core IT asset. Ensure that updates, patches and security policies are consistently applied across all distributed nodes.