Distributed Edge Computing: Unlocking the Power of Decentralized Networks To Drive Innovation

Distributed edge computing empowers organizations to handle data processing efficiently, flexibly and securely at scale. In this article, we will explore what distributed edge computing is, how it works, and how it compares to traditional edge strategies. We will also examine its benefits, challenges and real-world applications. Finally, you’ll learn how distributed edge computing can help you unlock value right where your data is created.

Defining distributed edge computing

Distributed edge computing combines edge computing and distributed computing to bring data-processing capabilities closer to where data originates. Traditionally, businesses have relied on centralized data centers or cloud infrastructures for processing. However, now that more devices are generating data in real time, shipping all that information back to a central server can create latency, bandwidth  and security issues. Distributed edge computing deploys compute and storage resources at or near the locations where data is generated. This significantly speeds up analysis and decision-making.

Edge computing

Before diving deeper into distributed edge computing, it is important to understand edge computing. Edge computing refers to the practice of processing data as close as possible to the source of that data. Instead of sending every piece of information to a centralized data center, you process most or all of it locally. This reduces latency, saves bandwidth and improves performance to drive real-time decision-making 

Because edge computing is distributed by design, it reduces your dependency on a single location for processing. However, a typical edge architecture might still involve a limited number of edge nodes or rely on a handful of remote or mobile sites. This is where distributed edge computing comes in.

Distributed computing

In distributed computing, computing resources (e.g., CPU, memory, storage and networking) are spread across multiple physical machines that communicate over a network. It spreads workloads more efficiently, improves fault tolerance and allows for horizontal scaling of applications and services. 

By blending distributed computing with the edge, you can create a mesh of interconnected nodes that serve different edge locations. The entire setup can be managed under a unified framework. That way, you can apply consistent security policies, resource provisioning, updates and monitoring.

How does distributed edge computing work?

Distributed edge computing uses technologies from both the edge and distributed systems. It combines them into an architecture that processes data locally at numerous remote or branch locations. At each location, you deploy a small footprint of hardware with sufficient compute, memory and storage resources to handle local workloads. Examples of these workloads include containerized applications, data analytics or machine learning models.

This localized infrastructure then connects to either a central data center or cloud service (or even multiple clouds). The local node analyzes or partially processes data. It then shares only relevant, aggregated or compressed information upstream. 

To manage hundreds or thousands of these sites efficiently, you need effective orchestration and management tools. Container orchestration platforms (often based on Kubernetes) are commonly used.

How is distributed edge computing different from standard edge computing?

Both distributed edge computing and standard edge computing aim to move computation close to where data is generated. However, their scope and scale differ.

• Scope of deployment: Standard edge computing may involve a small number of edge nodes. Each node handles localized tasks in a specific region or facility. Distributed edge computing involves numerous nodes spread across many geographic regions.
• Coordination and orchestration: Standard edge solutions can be managed individually or with a lightweight centralized control system. Distributed edge computing requires more advanced orchestration and automation to handle massive scale. 
• Resiliency: When you expand to a large number of edge locations, the chance that one node might encounter failures or lose connectivity increases. A distributed approach incorporates failover and redundancy measures across all sites. As a result, local operations can continue even if a node goes offline.
• Hybrid integrations: Distributed edge computing often integrates with multiple cloud platforms, local data centers and partner ecosystems. This multi-environment integration can be more complex than traditional edge solutions.
• Data governance: In a standard edge scenario, you might keep some local data for compliance or real-time analysis. With distributed edge computing, each site can maintain its own operational data. It selectively sends aggregated insights to the cloud.

The benefits of distributed edge computing 

The advantages of distributed edge computing continue to expand, especially as data volumes grow and real-time analytics become even more critical. Some of the top benefits include:

1. Reduced latency and faster responses: By processing data close to its source, you can reduce the round-trip time to a central cloud or data center. This is critical for applications where quick decisions are necessary such as autonomous robots, remote patient monitoring or online gaming.
2. Bandwidth optimization: When computation is distributed, only relevant data goes back to a central server for deeper analysis. You can filter, aggregate or compress it at the source, which lowers bandwidth costs and frees up network capacity.
3. Scalability at large volumes: It is relatively straightforward to manage a handful of edge nodes. However, if your business needs hundreds or thousands of geographically dispersed sites, you will require a distributed architecture with centralized orchestration to ensure scalability without excessive operational complexity.
4. Improved reliability and resilience: Distributed edge nodes can keep functioning even if the connection to the central data center or cloud is lost. This makes the entire system more fault tolerant and allows local options to continue. 
5. Enhanced data privacy and compliance: Processing data locally allows you to comply with regulations that require data to remain in a specific region or country. Sensitive or personally identifiable information can be analyzed at the edge and never stored in the cloud.
6. Reduced operational costs: Deploying hardware at multiple locations may seem costly. However, the efficiencies gained in bandwidth reduction, lower cloud compute usage and real-time optimization can offset those investments. Over time, you could see a net savings, especially if your cloud data ingestion costs are high.
7. Better user experience: If you serve customers across many regions, local processing can dramatically improve application responsiveness. Users benefit from less network-induced lag and more consistent performance (regardless of how far they are from your primary data centers).

Common challenges with distributed edge computing

Distributed edge computing introduces a unique set of challenges worth considering and planning for.

1. Complex orchestration: Managing a large fleet of edge nodes across geographies requires a powerful, unified platform. You need to ensure that software updates, security patches, and policy changes are applied consistently which can overwhelm your IT teams.
2. Security concerns: Each edge node can become an entry point for cyberattacks if not secured properly. Distributed architectures increase the number of potential vulnerabilities. For that reason, it’s important to implement identity management, encryption and zero-trust networking measures.
3. Scalability of infrastructure: Local compute and storage resources at edge nodes may be limited by physical space, power constraints or budget. It can be challenging to plan capacity effectively and allow for future expansion in remote environments.
4. Reliability of connectivity: Although local processing reduces the need for constant cloud communication, many distributed edge applications still rely on intermittent connectivity for data aggregation, updates or coordination. Poor or unreliable connectivity can lead to partial system failures if the edge node depends on external resources at critical moments.
5. Regulatory compliance and data governance: Different regions can have varying rules for data storage, access and privacy. Ensuring that local nodes comply with local regulations (and that aggregated data in the cloud also meets relevant laws) requires ongoing monitoring.
6. Operational overhead: Even with strong automation tools, physically managing hardware at remote sites can be logistically complex. Maintenance tasks may require specialized staff.
7. Skill gaps: Integrating distributed computing with edge deployments demands expertise in systems administration, networking, Kubernetes orchestration and cloud services. Your IT team may need additional training or external support to succeed.

Distributed edge computing in the real world

In each of the real-world use cases below, edge nodes handle computationally intensive tasks locally. Only selected data or insights is sent to a central location, which keeps the system lean and agile.

1. Smart retail: Large retail chains with hundreds of stores can use distributed edge computing to analyze in-store foot traffic patterns, inventory levels and customer behavior data locally. Each store can then tailor promotions, restocking processes to improve customer experience and inventory management.
2. Manufacturing and industrial automation: In factories, real-time monitoring of machinery performance is crucial for predictive maintenance and operational efficiency. Distributed edge nodes can run analytics that detect anomalies in milliseconds. This local autonomy prevents costly downtime while sending only critical data to a centralized platform.
3. Healthcare and telemedicine: Distributed edge computing supports remote patient monitoring devices and telehealth solutions. By processing patient data on-site, providers can make informed, fast diagnoses and treatment recommendations. Sensitive health information can remain local to comply with data privacy regulations. Aggregated insights can still flow back to central systems for research and analytics.

As you design or refine your edge architecture, check out more detailed edge computing use cases for inspiration.

What do I need to implement distributed edge computing?

Successfully implementing distributed edge computing requires more than just hardware at remote locations. Here are the key components you will need:

1. Efficient hardware at the edge with each edge site having sufficient processing power, memory, storage and networking capabilities.
2. Container orchestration platform to help you manage many edge nodes consistently.
3. Centralized management and automation through A single-pane-of-glass management solutions like SUSE Edge.
4. Network infrastructure, designed for partial connectivity, so critical tasks can still run locally if the connection to the central data center or cloud goes down. 
5. Security and identity management through zero-trust and strict access controls.
6. Local data storage and analytics.
7. A centralized observability platform to spot trends, anomalies or potential capacity issues.
8. Edge-optimized workloads.

The path to success with distributed edge computing

Distributed edge computing pushes intelligence and analytics right to the source, whether that source is a remote manufacturing line or retail storefront. Distributing workloads across many edge nodes offers a wealth of benefits including:

• Reduced latency
• Optimized bandwidth
• Compliance with data governance requirements
• Improved user experiences

Of course, adopting distributed edge computing comes with its own challenges. To succeed, you must prepare for and address the complexities that come with managing many remote sites and securing numerous endpoints. You’ll also need to maintain consistent configurations. The key is to plan carefully, follow proven best practices and scale up methodically. That way you can maintain reliability and security even as you deploy thousands of edge nodes.

At SUSE, we recognize the transformative power of distributed edge computing. That’s why we’ve designed SUSE Edge, so you can effectively manage containerized applications across distributed infrastructures. It brings together centralized controls with local autonomy so you can realize the full promise of edge-based innovation. Whether you are just starting to explore a distributed edge strategy or you are scaling an existing initiative, SUSE Edge is here to empower you on the path to success.

Frequently asked questions about distributed edge computing

What is the role of edge computing in distributed systems?

Edge computing brings computation and data storage closer to where the data is generated in a distributed system. Instead of relying only on centralized data centers or clouds, edge devices or nodes handle local workloads. They analyze data, run applications and perform machine learning in real time. By offloading some tasks from the central infrastructure, you reduce network latency and bandwidth usage. 

This local autonomy also increases the resilience of the overall distributed system. If the central site goes offline, the edge nodes can continue functioning and making real-time decisions. 

How can distributed edge computing benefit businesses?

Distributed edge computing improves responsiveness, lowers operational costs and addresses data governance needs. Say you own a retail business. With edge computing, you can process sales and customer behavior data locally in each store. Doing so will reduce costs as there is no need for data transfers to a central data center. At the same time, you can tap into real-time analytics, which will allow you to adjust sales or restock items proactively. 

Edge computing will also mitigate network congestion issues, minimizing latency. Finally, local autonomy increases reliability. If one of your sites has a connectivity issue, operations at your other locations will not be affected. 

How can edge computing contribute to energy efficiency?

Through edge computing, you can reduce network usage and consequently energy consumption. This is done by avoiding moving large volumes of raw data to a central data center or cloud for analysis. You can also optimize workloads dynamically, turning certain local resources on or off based on real-time demand. 

Edge computing can also be used to power advanced monitoring of systems like HVAC units, industrial equipment or power grids. By analyzing conditions locally, you can make more precise adjustments to save energy. 

How can IoT be incorporated to expand edge computing’s capabilities?

Many distributed edge environments go hand in hand with Internet of Things (IoT) implementations. If your organization already uses IoT sensors or connected devices, consider building IoT edge computing in your future efforts. This integration will also help you to further incorporate real-time analytics and automation into your edge strategy. By processing IoT-generated data as close to the device as possible, you reduce latency and bandwidth usage. You will also enhance your ability to act on insights right away.