Fog and Edge Computing for Faster, Smarter Data Processing

As organizations handle growing volumes of data from IoT devices and distributed systems, traditional cloud computing faces challenges related to latency, bandwidth use and real-time processing needs. Both fog and edge computing aim to bring processing closer to data sources, but they work as different architectural layers with distinct characteristics. This article explains these differences and helps you choose the right approach for your infrastructure needs.

Edge computing explained

Edge computing involves performing computation at or near the physical location where data starts. This means that processing happens directly on IoT devices, sensors or local gateways rather than sending all data to distant cloud servers. Edge computing dramatically cuts down the distance data must travel, resulting in faster response times and lower bandwidth use.

For example, when a smart factory sensor detects a temperature problem, edge computing allows immediate analysis and response without waiting for data to reach a centralized data center. Edge devices analyze information on their own, make real-time decisions and only send relevant results to the cloud when needed.

Fog computing explained

Fog computing works as an intermediate layer between edge devices and cloud infrastructure. Originally coined by Cisco, the term “fog” is like clouds close to the ground – computing resources positioned between the network edge and centralized data centers. Fog computing pushes processing to the Local Area Network (LAN) level, collecting data from multiple edge devices before deciding what information needs cloud storage.

Rather than processing data directly on individual devices, fog computing uses fog nodes or IoT gateways to collect, filter and analyze information from multiple sources. These fog nodes act as smart go-betweens that can process data locally, store frequently accessed information and forward only critical data to the cloud for long-term storage or deeper analysis.

This setup creates a distributed computing environment where fog nodes handle big processing workloads while staying connected to both edge devices and cloud services.

The similarities between fog and edge computing

To make an informed decision between fog and edge computing, it’s important to first understand how they are alike. Both approaches address the growing challenges of IoT data management and offer compelling alternatives to traditional cloud-only setups.

Reducing latency and improving response time

Both fog and edge computing cut down the time needed for data to travel from its source to processing infrastructure. Manufacturing systems, healthcare monitoring and autonomous vehicles benefit a lot from this reduced latency.

Minimizing data transport and saving bandwidth

Instead of sending every piece of raw data to distant cloud servers, both fog and edge computing process information locally and send only relevant results. Organizations with lots of IoT devices particularly benefit from these bandwidth savings.

Enhancing security and data privacy

By keeping sensitive information closer to its source, both approaches cut down exposure to network-based threats. This helps organizations keep better control over their data while meeting compliance regulations.

Fog computing vs. edge computing: A detailed comparison

While fog and edge computing share common goals, they differ a lot in how they work, their scope and how they’re set up. Understanding these differences is another important step that can help you choose the best approach for your specific needs.

Major differences

Edge computing processes data directly on devices and sensors, while fog computing uses LAN-level nodes and gateways. Edge computing focuses on individual device processing with minimal connectivity needs, whereas fog computing collects data from multiple devices and needs local network infrastructure. Edge computing gives you lower setup complexity and device-by-device scaling, while fog computing has enhanced processing power but with higher setup complexity.

Location of computation

Edge computing handles data analysis directly on the devices that create information – sensors, cameras, industrial controllers or IoT endpoints. Fog computing processes data at middle points within the local network infrastructure, collecting information from multiple edge devices before doing analysis.

Architecture and scope

Edge computing focuses on individual devices with built-in processing capabilities. Fog computing creates a layered setup where fog nodes serve multiple edge devices within a defined network segment, offering a more complete view of system operations.

Latency

Both approaches cut down latency compared to cloud-only setups, but edge computing usually offers lower response times. Since edge processing happens directly on data-creating devices, there’s no network delay for initial analysis. Response times are usually milliseconds rather than seconds.

Fog computing adds minimal extra latency as data travels from edge devices to fog nodes within the local network. However, this slight delay is often okay given the enhanced processing capabilities of fog nodes. For applications that need an absolute minimum latency, edge computing works better.

Security

Edge computing offers strong security through data locality – information stays on the device that creates it. However, this approach needs solid security measures on many distributed devices. Fog computing centralizes security management within the fog layer while keeping local data processing, making security policy management easier compared to handling security on hundreds of individual edge devices.

When to use edge vs. fog computing

Choosing between edge and fog computing depends on your specific business needs, infrastructure constraints and operational goals.

Ideal use cases for edge computing

Edge computing works great in cases that need immediate responses with minimal infrastructure dependencies. IoT edge computing applications benefit when devices must work on their own or in environments with limited connectivity.

Autonomous vehicles are a perfect edge computing application. Sensor data from cameras, lidar and radar must be processed instantly to make driving decisions. Industrial robotics and manufacturing automation also use edge computing well, with production equipment analyzing operational data and adjusting parameters in real-time.

Remote monitoring applications, such as oil pipeline sensors or agricultural equipment, benefit from edge computing’s ability to work on its own. These devices often function in areas with limited or spotty connectivity, making local processing capabilities crucial for continuous operation.

Ideal use cases for fog computing

Fog computing works best for applications that need coordination between multiple data sources and more sophisticated analysis than individual devices can handle. Smart city infrastructure, building management systems and large-scale IoT deployments often benefit from fog setups.

Traffic management systems are another good use case for fog computing. Multiple traffic sensors, cameras and control systems create data that fog nodes can analyze together to optimize traffic flow across entire city districts. This coordinated approach provides better results than individual intersection devices acting on their own.

Manufacturing facilities with complex production lines benefit from fog computing’s ability to collect data from many machines, sensors and quality control systems. Fog nodes can spot patterns across the entire production process, optimize resource allocation and predict maintenance needs based on comprehensive operational data.

Retail environments use fog computing to analyze customer behavior, inventory levels and environmental conditions across entire store networks. Regional fog nodes can process data from multiple locations, making coordinated inventory management and customer experience optimization possible.

Building a cohesive edge-to-cloud architecture with SUSE

Modern distributed computing environments don’t require you to choose between edge, fog and cloud computing. These approaches work together in comprehensive setups that use each technology’s strengths. Open source edge computing platforms give you the flexibility to use hybrid solutions that scale from device-level processing to enterprise-wide analytics.

SUSE Edge offers a complete platform for managing distributed computing workloads across the entire infrastructure spectrum. Built on open source foundations, SUSE Edge combines lightweight Kubernetes distributions, streamlined device management and enterprise-grade security to support both edge and fog computing deployments.

The platform lets organizations deploy consistent runtime environments from individual edge devices to fog nodes and cloud infrastructure. This consistency makes application development, deployment and management easier while giving you the flexibility to optimize processing location based on specific workload needs.

SUSE Edge supports the full spectrum of distributed computing scenarios. Organizations can start with simple edge deployments on individual devices, expand to fog computing for coordinated local processing and integrate with cloud services for comprehensive data analytics and long-term storage.

Ready to set up a unified edge-to-cloud setup? Explore SUSE Edge solutions to discover how open source platforms can improve your distributed computing strategy.

Fog and edge computing FAQs

Can you have edge computing without fog computing?

Yes, edge computing can work on its own without fog computing infrastructure. Many applications process data directly on edge devices and send results to cloud services, skipping fog layers entirely. This approach works well for scenarios that need minimal latency and simple device-level decisions.

Is fog computing a type of edge computing?

Fog computing is related to edge computing but works as a distinct approach. While both bring processing closer to data sources, fog computing creates a middle layer between edge devices and cloud infrastructure, whereas edge computing processes data directly on endpoint devices.

What is the relationship between fog computing and the cloud?

Fog computing extends cloud capabilities to local network environments, creating a bridge between edge devices and centralized cloud infrastructure. Fog nodes collect data from multiple edge sources, do local analysis and selectively forward information to cloud services for storage and further processing. This relationship lets organizations optimize data flows and cut down cloud dependencies while keeping access to comprehensive cloud services.