An Introduction To Edge Computing

In today's business world, data is the lifeblood that provides valuable business insight and supports real-time control over critical business processes and operations. With the advent of sensors and IoT devices that operate in real-time from remote locations, huge amounts of data can be routinely collected almost anywhere in the world. This virtual flow of data is also changing the way that businesses operate.

In this article, we will discuss the concept of edge computing. We are going to see what exactly edge computing is. We’ll cover how it works with use cases and examples. Finally, we'll jump into the benefits and challenges you should remember.

What Is Edge Computing?

So, what exactly is Edge Computing? Edge computing is a distributed information technology architecture that processes client data at the periphery of the network, as close to the original source as possible. In simplest terms, Edge Computing moves some portion of storage and compute resources out of the central data center and closer to the source of data itself. Rather than transmitting raw data to a central data center for processing and analysis, that work is instead performed where the data is actually generated. Whether it's a retail store, a factory floor, a sprawling utility, or across a smart city, Edge Computing enables data processing and analysis to happen closer to where it's needed.

Brief History of Edge Computing

The concept can be traced back to the 1990s when Akamai launched its content delivery network (CDN). The idea was to introduce nodes closer to end-users' geographic locations to deliver images and videos efficiently. In 1997, Akamai demonstrated how web browsers, video, and speech recognition applications running on resource-constrained mobile devices could offload certain tasks to powerful servers. The objective was to reduce the computing load and improve battery life.

Today, speech recognition services from Google, Apple, and Amazon work in a similar way. In 2001, scalable and decentralized distributed applications were used to propose different peer-to-peer overlay networks. These self-organizing overlay networks facilitated efficient and fault-tolerant routing, object location, and load balancing. Furthermore, these systems made it possible to exploit the network proximity of underlying physical connections in the internet, avoiding long-distance links between peers. This decreased the overall network load and improved the latency of applications.

How Does Edge Computing Compare with Cloud Computing

Cloud computing was a significant influence in the history of edge computing and deserves special mention. It gained significant attention in 2006 when Amazon first promoted its elastic compute cloud, opening up new opportunities in computation, visualization, and storage capacity. However, cloud computing wasn't the solution in all use cases. With the advent of self-driving cars and IoT, local processing of information became increasingly important to enable instantaneous decision-making.

In 2009, the term "cloudlet" was introduced. The major focus was on latency, with a two-tier architecture. The first tier, known as the cloud, has high latency, and the second, known as cloudlets, has lower latency. Cloudlets are decentralized, widely dispersed internet infrastructure components.

Their compute cycles and storage resources can be leveraged by nearly mobile computers. Moreover, a cloudlet only stores soft-state, such as cached copies of data.

In 2012, Cisco introduced the term "fog computing" for dispersed cloud infrastructures. The aim was to promote IoT scalability to handle a massive number of IoT devices and big data volumes for real-time, low-latency applications. Today, an IoT solution must cover a much broader scope of requirements. In most cases, organizations opt for a combination of cloud and edge computing for complex IoT solutions. Cloud computing comes into play when organizations require storage and computing power to execute certain applications and processes and to visualize telemetry data from anywhere. On the other hand, edge computing is the right choice when low latency, local autonomous actions, reduced back-end traffic, and confidential data are involved.

Now that we have a rough idea about how edge computing came into the picture, let's move on to explore its current state and future prospects.

How Does Edge Computing Work

Let's take a look at how edge computing works. The principle is straightforward: if you can't bring the data closer to the data center, bring the data center closer to the data. This principle forms the basis of edge computing. Although it is a new concept, it draws on decades-old ideas of remote computing, such as remote offices and branch offices. Placing computing resources at the desired location has proven to be more reliable and efficient than relying on a single central location.

Edge computing plays a pivotal role in ensuring that all devices processing IoT, real-time, and big data are connected to the main cloud data center through edge cloud computing platforms. it's essential to understand why we need edge computing, and there are two main factors: latency and processing.

For instance, when you talk to your Alexa device, you may experience some delay as it retrieves data from the cloud provider. However, when driving a connected or self-automated car, a delay in communication with the cloud provider can have severe consequences. Edge computing came into the picture as a driving force for 5G networks, which enables faster data processing.

In edge computing, three players are involved: the cloud data center, edge gateway server, and edge client devices. The edge client device is installed on all systems, recording various data generated by devices, processing it with built-in intelligence and storage capacity. For higher workloads, it sends the data to the nearby gateway server, which is physically located close to the device, reducing latency and increasing processing power.

Examples Of Edge Computing

The trend of edge computing is increasing, and it is being used in various sectors. Let’s explore some interesting edge computing use cases and examples.


An industrial manufacturer implemented edge computing to monitor production and enable real-time analytics and machine learning at the edge. This helped identify production errors and improve product quality. Environmental sensors were added throughout the manufacturing plant to provide insights into how each product component is assembled, stored, and how long it remains in stock. The manufacturer can now make faster and more accurate business decisions regarding the facility and manufacturing operations.


Consider a business that grows crops indoors without sunlight, soil, or pesticides. The process reduces grow times by more than 60%. Sensors enable the business to track water use, nutrient density, and determine optimal harvest times. Data is collected and analyzed to find the effects of environmental factors and continually improve the crop growing algorithms to ensure that crops are harvested in peak conditions.

Network optimization

Edge computing can help optimize network performance by measuring user performance across the internet and employing analytics to determine the most reliable, low-latency network path for each user's traffic. In effect, edge computing is used to steer traffic across the network for optimal time-sensitive traffic performance.


The healthcare industry has drastically expanded the amount of patient data collected from devices, sensors, and other medical equipment. The enormous data volume requires edge computing to apply automation and machine learning to access the data, identify normal data, and identify problem data. Clinicians can then make immediate decisions to help patients avoid health incidents in real-time.


Autonomous vehicles require and produce massive amounts of data, ranging from 5 dB to 20 TB per day. Information about location, speed, vehicle condition, road condition, traffic conditions, and other vehicles must be aggregated and analyzed in real-time while the vehicle is in motion. Each autonomous vehicle becomes an edge, and the data can help authorities and businesses manage vehicle fleets based on actual conditions on the ground.

Retail stores

Retail businesses produce enormous data volumes from surveillance, stock tracking, sales data, and other real-time business details. Edge computing can help analyze this diverse data and identify business opportunities, such as effective end cap campaigns, predict sales, and optimize vendor ordering. Since retail businesses can vary dramatically in local environments, edge computing can be an effective solution for local processing at each store.

Benefits of Edge Computing

Let's explore some of the benefits that edge computing bring to the table.


Edge computing is particularly useful in areas where connectivity is unreliable, or bandwidth is restricted due to environmental characteristics, such as oil rigs, ships at sea, remote farms, rainforests, and deserts. Edge computing performs computations on-site, sometimes directly on the edge device itself, such as water quality sensors on water purifiers in remote villages. This approach saves data to transmit to a central point only when connectivity is available. By processing data locally, the amount of data to be sent can be significantly reduced, requiring far less bandwidth or connectivity time that might otherwise be necessary.

Data sovereignty

Moving large amounts of data is not just a technical problem. The journey of data across national and regional borders can pose additional issues, including data security, privacy, and legal concerns. Edge computing can be used to keep data close to its source and within the bounds of prevailing data sovereignty laws, such as the European Union's GDPR. This approach allows raw data to be processed locally, obscuring and securing any sensitive information before sending anything to the cloud or primary data center, which may be in other jurisdictions.

Data security

Although cloud providers offer IoT services and specialize in complex analysis, enterprises remain concerned about the safety and security of data once it leaves the edge and travels back to the cloud or data center. By implementing computing at the edge, any data traversing the network back to the cloud or data center can be secured through encryption, and the edge deployment itself can be hardened against hackers and other malicious activities, even when security on IoT devices remains limited.

Challenges Of Edge Computing

Now, let's take a look at the challenges of edge computing.

Limited capability

While edge computing offers many benefits over cloud computing, such as a greater variety and scale of resources and services, deploying an infrastructure at the edge can be challenging. The scope and purpose of the edge deployment must be clearly defined, even if it serves a specific purpose at a predefined scale using limited resources and few services.


Edge computing can overcome typical network limitations, but even forgiving edge deployments will require a minimum level of connectivity. It's crucial to design an edge deployment that accommodates poor and erratic connectivity, and consider what happens when connectivity is lost. Autonomy, AI, and graceful failure planning are essential to successful edge computing in the wake of connectivity problems.


IoT devices are notoriously insecure, so it's essential to design an edge computing deployment that emphasizes power device management, such as policy-driven configuration enforcement, as well as security in the computing and storage resources, including software and patching updates. Encryption of data at rest and in-flight is also critical. While IoT services from major cloud providers include secure communications, this is not automatic when building an edge site from scratch.


Edge computing is a fast-growing technology that allows for faster decision-making, reduced costs, and real-time response by processing data closer to the source and as the number of connected devices increases, it will play a crucial role in enabling IoT and other emerging technologies. It’s a promising opportunity for organizations to benefit from distributed computing and achieve new efficiencies.

At Solwey, we understand technology and can leverage the most suitable tools to help your business grow and achieve your goals. Reach out if you have any questions about edge computing or technology in general, and find out how Solwey and our custom-tailored software solutions can cover your needs.

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