Edge Computing: What it is, Benefits, and How it Compares to Cloud

Edge computing is transforming how businesses handle data processing by decentralizing computation and bringing it closer to where data is generated—at the “edge” of the network. Rather than relying on centralized cloud servers, edge computing enables real-time data analysis and decision-making, reducing latency and improving performance. This guide explores the key concepts, applications, and insights about edge computing.

What is Edge Computing?

Edge computing is a distributed computing model in which computing takes place near the physical location where data is being collected and analyzed, rather than on a centralized server or in the cloud. Edge Computing enables existing control and automation applications to be consolidated onto a single platform while enabling other critical applications to run on the same platform. This makes it easier to develop smart, Industrial Internet of Things (IIoT) enabled machines, and to easily add future applications that support customers’ evolving Industry 4.0 and Smart Manufacturing demands.  

Edge Computing offers opportunities to improve business and production processes. Decision making is made more efficient and more resilient through real-time visibility into operations. Simply put, Edge Computing accomplishes this step change in operational performance by bringing computing resources into close proximity with the critical equipment where operational data is generated and applied. 

Why is Edge Computing Important?

Edge computing is important because it creates new and improved ways for industrial and enterprise-level businesses to maximize operational efficiency, improve performance and safety, automate all core business processes, and ensure “always on” availability. It is a leading method to achieve the digital transformation of how you do business.

Increasing computing power at the edge is the foundation needed to establish autonomous systems, enabling companies to increase efficiency and productivity while enabling personnel to focus on higher value activities within the operation.

How does edge computing work?

Edge computing works by processing data closer to its source rather than sending it to a centralized data center or cloud server for processing. Here’s how the process typically works:

• Data Generation at the Edge: Devices such as IoT sensors, cameras, machines, or smartphones generate massive amounts of data. This could be anything from sensor readings, video footage, or other real-time metrics.
• Local Processing on Edge Devices: Instead of sending this data to a remote cloud server, edge computing enables the data to be processed locally, either directly on the device (like an IoT sensor) or on a nearby edge server (often called an “edge node”). This local processing can involve filtering, analyzing, and making decisions based on the data in real-time.
• Real-Time Decision Making: By processing data locally, edge computing enables instant decision-making. For example, a smart traffic light system can adjust traffic flow based on real-time data from nearby sensors without waiting for instructions from a distant data center.
• Sending Relevant Data to the Cloud (if needed): After local processing, only the most important or aggregated data may be sent to the cloud or central data centers for further analysis, storage, or long-term processing. This reduces the bandwidth required and ensures that only necessary data is transmitted.
• Feedback Loop for Continuous Improvement: In many cases, edge computing systems also allow for real-time learning and improvement. For example, AI models running at the edge can be updated as they learn from new data, enabling continuous optimization of the system’s performance.

In summary, edge computing brings computational power closer to the “edge” of the network (where the data is generated), which leads to faster processing, reduced latency, lower bandwidth use, and enhanced security by limiting the amount of data sent to centralized servers.

Edge Computing Architecture

Edge computing architecture refers to a distributed computing framework that processes data closer to where it is generated, rather than relying solely on centralized data centers. This approach reduces latency, increases efficiency, and optimizes the use of network resources. In an edge computing setup, data is processed at or near the “edge” of the network—often on devices like sensors, gateways, or local servers—before being sent to the cloud or centralized data center for further analysis if necessary.

Edge computing architecture consists of several key components working together to optimize data processing and decision-making in real time:

• Edge computing devices: Generate real-time data (IoT sensors, cameras, etc.).
• Edge computing nodes: Perform local processing and real-time analytics.
• Edge gateways: Manage data traffic and facilitate communication between edge devices, nodes, and the cloud.
• Cloud data centers: Handle storage, complex analytics, and synchronization.
• Control and management layer: Ensures system operation, security, and scalability.
• Communication networks: Enable data transfer across all system components.

Edge computing Advantages and Disadvantages

Benefits of Edge Computing 

One of the top benefits of implementing edge computing is the ability to collect and analyze data where it is collected, catching and correcting problems that might not be identified as quickly if the data were to be sent to a central server or cloud for processing and analysis. Keeping data on site also reduces the security risk associated with porting data, which can be important in financial organizations, for example. It also reduces bandwidth costs by processing some data on site, rather than sending all data to a cloud or central server.

Advantages of Edge Computing

• Low Latency:

Edge computing processes data closer to its source, reducing the time it takes to analyze and make decisions. This is critical for applications that require real-time data, such as autonomous vehicles and industrial automation.

• Improved Bandwidth Efficiency:

By processing data locally, edge computing reduces the amount of data that needs to be sent to centralized cloud servers, optimizing bandwidth usage and reducing costs.

• Enhanced Security and Privacy:

Sensitive data can be processed locally, reducing the risk of data breaches during transmission to the cloud. Edge computing helps ensure compliance with privacy regulations by keeping data closer to its origin.

• Reliability and Resilience:

Edge computing can function independently of centralized cloud servers, making it more reliable in areas with intermittent or poor internet connectivity.

• Scalability:

As more devices are added to a network, edge computing can easily scale to handle increasing data processing demands without overwhelming centralized cloud systems.

Challenges in Edge Computing

Successful edge computing requires a thoughtful architecture and implementation , which can be a challenge without the right expertise. Having multitudes of sites collecting and analyzing data can mean more sites that need to be configured and monitored, adding complexity. Having too few can mean critical data is missed. Decentralized locations can also mean fewer technical personnel on site, meaning non-technical operations staff may be called in to troubleshoot. These challenges can be addressed by working with knowledgeable system integrators and using the right edge technology.

Disadvantages of Edge Computing

• Limited Computing Resources:

Edge devices often have limited processing power compared to centralized data centers. This can limit the complexity of applications and AI models that can be deployed at the edge.

• Increased Management Complexity:

Managing a distributed network of edge devices can be more complex than managing a centralized cloud environment. It requires robust monitoring and maintenance strategies.

• Security at the Edge

Because edge computing is distributed, the security risk is different than a centralized environment. The security controls found in private data centers or public clouds, like firewalls or antivirus tools, don’t automatically transfer. Experts recommend a few simple steps, including hardening each host, real-time network monitoring, encrypting data, and adding physical security measures.

• Data Synchronization:

Ensuring that data and AI models are up-to-date across all edge devices can be a challenge, especially as the number of devices grows.

Edge computing comparison with Cloud

Edge computing and cloud computing are both crucial technologies that enhance data processing, storage, and analysis, but they work in fundamentally different ways. Understanding the differences between the two can help businesses choose the right solution based on their specific needs for performance, efficiency, and scalability.

Cloud Computing: Centralized Power and Scalability

• Centralized: Data is processed and stored in large, centralized data centers.
• High Latency: Due to the distance between data sources and data centers, there can be significant latency, especially for real-time applications.
• High Bandwidth Requirements: Large amounts of data need to be transmitted to and from the data center, increasing network traffic.
• Security Concerns: Centralized data centers can be attractive targets for cyberattacks, making data security a major concern.
• Best Suited For: Large-scale data processing, storage, and analytics, as well as applications that do not require real-time processing.

Edge Computing: Localized Processing for Real-Time Data

• Distributed: Data is processed and stored closer to the source, at the edge of the network.
• Low Latency: By processing data locally, edge computing significantly reduces latency, making it ideal for real-time applications.
• Reduced Bandwidth Requirements: Less data needs to be transmitted over the network, optimizing bandwidth usage.
• Enhanced Security: Data is processed and stored closer to the source, minimizing the risk of data breaches.
• Best Suited For: Real-time applications, IoT devices, autonomous vehicles, and other applications that require low latency and high bandwidth.

Key Differences

Feature	Cloud Computing	Edge Computing
Data Processing Location	Centralized data centers	Edge of the network
Latency	High	Low
Bandwidth Requirements	High	Low
Security	Potential security risks	Enhanced security
Best Suited For	Large-scale data processing, storage, and analytics	Real-time applications, IoT, autonomous vehicles

Does the Edge Replace the Cloud?

Edge computing works hand in hand with the cloud to provide a flexible solution based on the data collection and analysis needs of each organization. For real-time collection and analysis, the edge is ideal for certain workloads. At the same time, the cloud can provide a centralized location for large scale analytics.  Together they provide real-time and longer term insights into performance and power initiatives like machine learning and asset performance management.

Hybrid Cloud and the Edge

If you’re already using a hybrid cloud architecture, then you’re familiar with the benefits of partitioning data between public and private clouds. Edge computing can be a great addition to this existing network. There are different configurations, and all work well, depending on your business goals and usage. For example, the edge can take the place of the private cloud, taking the primary computing role, or you can pair the edge with an existing hybrid cloud with both public and private clouds.

Edge Computing vs. Fog Computing

Both edge computing and fog computing are distributed computing models designed to process data closer to the source of generation rather than relying on centralized cloud servers. These technologies are often compared due to their similar objectives, but they differ in how they manage data processing, storage, and network communication. Understanding the distinctions between them is crucial for organizations deciding which model best fits their needs.

Edge computing refers to the practice of processing data directly on the devices or local systems at or near the data source. In edge computing, data is processed in real time on local devices such as IoT sensors, gateways, or edge servers. The goal is to reduce latency, minimize bandwidth usage, and enhance efficiency by handling tasks locally before sending only critical information to the cloud.

Fog computing acts as an intermediary layer between edge devices and the cloud. Rather than processing all data on the edge devices themselves, fog computing introduces a more distributed architecture where data is first sent to local fog nodes (usually routers or gateways) before being forwarded to the cloud or central data center for more advanced processing or storage. Fog computing extends the concept of edge computing but provides more networked resources for computation, storage, and analytics at the edge of the network.

Edge computing applications

The Future of Edge Computing

Edge computing is revolutionizing the way data is processed, transmitted, and analyzed by moving computation closer to the source of data generation—such as IoT devices, sensors, and local networks. As the demand for faster data processing, reduced latency, and improved security grows, edge computing is set to become more integral in various industries, from healthcare to manufacturing and smart cities.

Key Trends Shaping the Future of Edge Computing:

• 5G Integration: The widespread deployment of 5G networks will significantly boost edge computing capabilities, enabling faster data transfer speeds and more reliable connections, which are crucial for real-time decision-making in industries like autonomous vehicles and smart factories.
• AI and Machine Learning at the Edge: AI models are increasingly being run directly on edge devices, allowing for real-time data analysis without the need to send all data to centralized cloud servers. This results in faster insights, lower bandwidth costs, and improved privacy.
• Security and Privacy Enhancements: Edge computing offers improved security by processing sensitive data locally, reducing the risk of data breaches and cyberattacks during transmission. In addition, data can be anonymized or encrypted before it ever leaves the edge device.
• IoT Expansion: As IoT devices continue to proliferate, edge computing will handle the increased data load by enabling decentralized processing. This will be crucial in industries where instant decisions and autonomous operations are required, such as smart grids, healthcare monitoring, and industrial automation.
• Distributed Cloud and Hybrid Architectures: Many organizations will adopt hybrid cloud models where edge computing works in tandem with centralized cloud systems. This ensures that large-scale processing can happen in the cloud, while low-latency, localized processing occurs on the edge.

Top Edge Computing Platforms

Stratus Edge Computing Platforms

Stratus Edge Computing platforms are simple, protected, and autonomous. Designed for operational environments, our platforms are easy-to-use, scalable, flexible, and reliable, enabling plug-n-play solutions that quickly transform infrastructure. Core benefits include:

• Easy software deployment via virtual machines for multiple sites.
• Faster commissioning with standardized, duplicable solutions.
• Pre-integrated virtualization and redundancy; no special cabling required.
• Industrial-grade, Class I Division 2 certified, fan-less design, –40 to 60°C, 10-95% humidity, vibration-resistant.

Microsoft Azure IoT Edge

Microsoft’s Azure IoT Edge is one of the leading edge computing platforms, allowing businesses to extend cloud capabilities to edge devices. It enables IoT devices to run Azure services such as AI, analytics, and machine learning models locally, improving response times and minimizing the need for continuous cloud connectivity.

Key Features:

• Seamless integration with Azure services.
• Local execution of cloud-based applications.
• Security features like device management and encryption.
• Container-based architecture for flexibility.
• Support for a wide range of devices and systems.

Amazon Web Services (AWS) IoT Greengrass

AWS IoT Greengrass is designed to enable edge computing in IoT applications, allowing devices to run AWS Lambda functions, process data locally, and sync with the cloud. It also facilitates real-time data processing and machine learning capabilities at the edge, making it ideal for businesses needing low-latency operations.

Key Features:

• Edge processing of IoT data with Lambda functions.
• Local machine learning and AI inference.
• Secure communication between edge devices and the cloud.
• Seamless updates and scaling options.
• Offline capabilities for uninterrupted operations.

Google Cloud IoT Edge

Google Cloud IoT Edge offers a set of tools for running AI, machine learning, and analytics models on edge devices. It helps businesses process large volumes of IoT data locally, which can then be integrated back into Google Cloud services for more complex tasks and long-term data storage.

Key Features:

• AI and machine learning capabilities on edge devices.
• Integration with Google Cloud’s data analytics and AI services.
• Real-time data processing and visualization.
• Secure deployment and management of edge workloads.
• Containerized applications for portability.

IBM Edge Computing

IBM provides a robust edge computing platform that integrates AI, machine learning, and data analytics for smarter decision-making at the edge. It is widely used across industries like manufacturing, retail, and healthcare, offering real-time data processing to optimize operations.

Key Features:

• Real-time edge analytics for intelligent decision-making.
• Scalable architecture for large IoT deployments.
• Integration with IBM Cloud for hybrid cloud solutions.
• AI-driven insights at the edge.
• Built-in security and compliance features.

Why Stratus Edge Computing Platforms?

Simple

Ease of Management – Deploying software on Stratus Edge Computing platforms is as easy as installing an image of your solution on a virtual machine. This makes it simpler to install and deploy applications across multiple locations or sites.

Faster Commissioning time – Once the solution you create is fully tested, it is even easier to standardize and duplicate across multiple sites, in any plant or facility.

Protected

Integrated Virtualization and Redundancy – The virtualization and redundancy capabilities of Stratus Technologies are all pre-integrated and configured out of the box. There are no package hardware pairs or clustering that need to be specially cabled together or configured.

Intrinsically Safe and Industrial Grade – the Stratus ztC Edge is Class I Division 2 certified, uses a fan-less design that can be wall or DIN rail mounted inside control panel, rated at –40 to 60 deg C, 10 to 95% humidity, with 5-500 Hz vibration. It is a dependable choice especially for mission critical applications.

Autonomous

System Health and Support – Stratus Edge Computing Platforms are equipped with 24×7 health monitoring, automatic update and patch management, and the ability to set thresholds, receive alerts, and review logs, and conduct predictive failure analysis— all of which enhances the total reliability of your solution.

Flexible Management – our ztC™ Advisor allows you to easily view the health and utilization of your entire Edge Computing platform inventory, helping to remotely triage issues, improve productivity, and mitigate risk. In addition, simplified backup and restore allows you to remotely backup and restore any Edge Computing platform.

Learn About Stratus Edge Computing Platforms

Servo Dynamics – A Master Distributor of Stratus in Viet Nam

Servo Dynamics, as the authorized master distributor of Stratus Technologies in Vietnam, offering the cutting-edge, fault-tolerant edge computing solutions from Stratus to local businesses. With a focus on ensuring continuous availability, seamless integration, and real-time data processing, Servo Dynamics ensures businesses in Vietnam access to some of the most advanced technologies available today, including ztC Edge and ftServer. 

Servo Dynamics is committed to supporting local industries through expert guidance, seamless deployment, and continuous support, ensuring that businesses in Vietnam can leverage the power of Stratus technology to thrive in today’s fast-paced, data-driven world.