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What is edge computing?

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This blog has been expertly reviewed by Jason Vigus, Head of Portfolio Strategy at Nasstar.

Edge computing has become an emerging concept over the past few years, and at the most basic level, it means moving computing power, networks and where data is managed away from centralised resources so that it’s closer to where it’s needed. To enable the processing of data closer to the user, edge computing uses a network of servers located in data centres around the world – or connected devices. 

Jason Vigus, Head of Portfolio Strategy at Nasstar said: “The primary benefit of edge computing is improved efficiency and speed of data handling. Instead of travelling to and from a single data source, information is processed locally, near where it's generated. But it’s not only about speed. End users see the benefits of a lower latency service, while businesses can reduce data transfer costs. Edge computing can also be useful in scenarios where bandwidth is extremely limited, or where there is no internet connection at all.” 

As organisations increasingly rely on data to make informed decisions, edge computing can provide several advantages. A recent McKinsey report predicts that edge usage will increase by double-digit percentages every year for the next five years 

How does edge computing work? 

Traditional computing relies on centralised data sources and infrastructure. Any incoming data or request for information must go all the way to that central source to be processed. Whilst being computationally efficient, this approach can lead to delays and bottlenecks (known as latency), since information sometimes has to travel long distances.  

Edge computing, on the other hand, uses numerous geographically distributed ‘nodes’, e.g. Internet of Things (IoT) devices, sensors, or end-user-devices, located as close to the action as possible. These quickly store and process generated data or hold copies of information replicated to and from a central source, able to serve it as and when needed. By placing edge nodes closer to the source of data generation, performance can be drastically improved. 

Why is edge computing important in modern technology? 

The need for edge computing comes from the increasing amount of data we produce. A prime example is the growing number of Internet of Things (IoT) services. A 2023 report estimated that there are currently 16 billion active, connected IoT devices. Each can constantly produce new data, sending it to central servers for storage. 

However, that traffic can start to overwhelm networks. Organisations can better manage their data flow by caching data on devices and placing edge computing solutions closer to the IoT systems. This brings several implications: 

  • It saves bandwidth: Edge devices can hold captured data, process it, and send only what is required to a central point.  
  • Lower latency: Likewise, edge devices can send information to nearby end users much faster than the central source could. 
  • Less dependency on connectivity: Edge solutions reduce the dependency on high-performing networks. By shifting the computer closer to the data source, edge solutions enable workloads to work in a predictable, smart way. 

Fundamentally, edge computing helps solve the issue of bandwidth and latency bottlenecks, bringing many advantages for businesses and users alike. 

Jason Vigus, Head of Portfolio Strategy at Nasstar commented: “Edge computing is a game-changer in the world of data processing, enabling faster and more efficient decision-making by bringing computing power closer to the source. Combined with the advancements in modern AI technology, edge computing is paving the way for a new era of innovation and progress in various industries.” 

What are the benefits of edge computing? 

Edge computing is impacting data processing with several significant benefits: 

  • Efficiency in processing data: Edge computing processes data locally, closer to the source. This results in faster and more efficient data handling, making applications more responsive and user-friendly. This is especially crucial for latency-sensitive applications. 
  • Enhanced customer experience: With data being processed closer to users, they experience faster and more reliable services. This proximity leads to a smoother and more satisfying user experience for both staff and customers. 
  • Cost savings: By localising data processing, only essential data is sent to central servers. This significantly reduces bandwidth usage, network strain and the associated costs of data transfer. 
  • Reduced risk: For critical scenarios (such as patient monitoring in a healthcare setting for example), edge computing enables the processing of key data to take place locally, regardless of connectivity availability to the centralised solution. 
  • Increased reliability and availability: Edge computing distributes processing and caching across multiple devices. This redundancy means if one node fails, others can step in to ensure business continuity. 
  • Adoption of new technologies: Using edge computing places businesses at the forefront of technological innovation. This positions them well to adapt to new developments and maintain technological relevance. For example, edge computing can significantly improve the training and processing times associated with machine learning and artificial intelligence. 

These benefits highlight the key drivers of edge computing adoption. While it can potentially transform how businesses handle data, there are also some considerations to make. 

Are there any challenges to using edge locations? 

Edge computing, while compelling, does present challenges that need consideration: 

  • Provisioning: Setting up edge locations involves careful planning and resource allocation. Companies need adequate computing power while not over-provisioning, which could bring unnecessary costs. 
  • Remote management: Unlike centralised storage, managing numerous edge locations can be a logistical challenge. These sites — often located across different regions — require effective remote management strategies to ensure they operate efficiently. 
  • Network dependence: Edge computing relies heavily on network connectivity, especially for IoT applications with centralised storage. In areas with limited fibre, 5G, or satellite network coverage, for example, the design of edge computing solutions must be carefully considered to overcome network dependence. 
  • Security: As a potential entry point for threats, each edge node needs robust cyber security measures. As the number of nodes increases, so does the complexity of defending them against the latest cyber threats.  

While edge applications bring challenges, careful planning and execution can help mitigate the most familiar challenges. Working with an experienced cloud services provider can also help mitigate potential issues. 

The components of edge computing 

Edge computing is a dynamic and developing field. However, it generally comprises a few pivotal elements. 

Cloud computing resources 

The underlying public cloud and hybrid cloud services sit at the heart of any workload, delivering computing power and storage space. Uniquely to edge computing, devices are also distributed and placed closer to the data's origin to enhance speed, efficiency, and reliability. 

IoT devices 

These devices are data gatherers in the edge computing environment. From household gadgets to wearables, we see many of them in our everyday lives. But their biggest impact is in industrial sensors, continuously collecting real-time data for immediate processing and decision-making. 

Edge servers 

Acting as localised information sources, these servers process data on-site. By doing so nearer to the source, they bring rapid response times and ease the strain and dependance on broader network resources. 


Reliable internet providers and technologies like 5G are fundamental to edge-of-the-network services. They provide a connection between IoT devices, edge servers, and cloud resources, enabling quick data transfer and near real-time processing. 


Of course, edge computing must have a positive impact. Users gain from accelerated and streamlined data processing, which is vital in applications demanding instant data analysis or interaction. 

Edge use cases — when should you use it? 

Edge computing is quickly becoming a foundational technology for many sectors. For each, it brings unique benefits tailored to specific challenges or needs. It particularly helps improve processes in the following areas: 

  • Manufacturing: The manufacturing sector uses edge computing for real-time data analysis. IoT sensors on production line machinery can immediately process information on-site, enabling quick decision-making that minimises efficiency and downtime. This is particularly helpful in tasks like predictive maintenance, or automated product quality control checks using AI vision. 
  • Healthcare: In healthcare, edge computing is vital in fields like patient monitoring and telemedicine. By rapidly processing patient data, it provides caregivers with instant insights. This is especially critical in remote locations where connectivity access may be limited or unreliable. 
  • Finance: Financial services benefit from edge computing through enhanced security and quicker data processing. It supports services like fraud detection and risk management and can hugely impact enterprise applications requiring real-time data transfer and processing. 
  • Transport: In transportation, edge computing services facilitate everything from real-time traffic management to autonomous vehicles with multiple sensors for navigation and collision avoidance. For instance, by processing data locally, a vehicle can respond to changing road conditions and make for safer, more cost-effective journeys. 

Each of these examples of edge computing demonstrates its potential benefits. By bringing data processing closer to where it’s needed, edge computing speeds up response times and opens new possibilities for innovation.  

Edge computing with the cloud 

Edge computing decentralises data processing away from centralised sources and towards local nodes. It can integrate cloud resources, IoT devices, edge servers, users, and networks to provide reduced latency, cost savings and an improved user experience. As organisations increasingly move towards a cloud-first strategy, they can employ edge computing to advance key business processes, products, and services.  

Nasstar's cloud managed services offer an adaptable approach to data management. They help ensure your cloud environment is not only well-architected and actively monitored but also optimised for the task at hand. Speak to a specialist to learn more about your edge computing options.  

Frequently Asked Questions

The 'edge' in edge computing describes how computing resources are dispersed geographically at the edge of networks. It uses processing power and service providers closer to data sources like IoT devices, where data management occurs. This reduces latency, minimises bandwidth consumption, and enhances efficiency compared to centralised data sources, for the same use cases.

Edge computing processes data closer to endpoints, whereas traditional computing uses a single, central data source. The edge approach reduces latency, improves response times and saves bandwidth. It’s particularly beneficial for real-time applications needing immediate data processing, or to efficiently manage traffic within a data-heavy environment. 

The edge computing model is used in various sectors, including manufacturing, healthcare, financial services, and transportation. It supports applications requiring quick data processing, like low-latency services. Likewise, real-time monitoring in manufacturing, telemedicine and autonomous vehicles are other important use cases. 

Edge computing is a crucial part of IoT scalability and sustainability. It allows for the processing of data from IoT devices at or near their location, rather than relying on distant servers. This local processing leads to faster decision-making and action, enhancing the effectiveness and efficiency of IoT apps.