Edge Computing: A Distributed Computing Architecture

9 min readAug 29, 2022

From 2021 to 2028, the worldwide edge computing industry is predicted to develop at a compound annual growth rate (CAGR) of 38.4 percent. Edge computing adds a layer of complexity to the organization by requiring a diverse set of stakeholders to handle IT infrastructure, traffic delivery, connectivity, application development, and service management. Edge computing also combines hardware, software solutions, and networking architecture to suit a wide range of use cases across various industries. Edge computing is projected to generate significant growth potential for newcomers soon, as the technology has been in the initial phases of development and its deployment and operational models are still to mature.

What is Edge Computing?

Edge computing is a distributed computing architecture that brings enterprise applications nearer to data sources like the Internet of Things devices or local edge workstations. It’s a networking strategy that focuses on bringing computation as close to the data source as feasible to reduce latency & bandwidth consumption. Edge computing, in simple terms, involves running fewer methods in the cloud and relocating them to local locations, such as a user’s computer, an IoT gateway, or an edge server. It’s a popular misperception that edge computing and Internet-of-things are interchangeable terms. Edge computing is a type of distributed computing that is sensitive to topology and location, and IoT is a use case for edge computing. Rather than referring to a single technology, the phrase refers to architecture. Faster insights, faster response times, and greater bandwidth availability can all be gained by edge computing.

Data normalization and analysis are used in the edge computing technique to find business analytics, with only the relevant data being sent to the main data center after analysis. Furthermore, the term “business intelligence” can refer to:

CCTV systems in retail establishments
Data on sales
Equipment installation and maintenance using predictive analytics
Generating power, maintaining product quality, and ensuring correct device operation, among other things.

Architecture of Edge Computing

Good architecture is required for computing jobs. For various computing activities, different architectures are necessary. And there is no “one-size-fits-all” policy here. Edge computing has evolved into a critical architecture for supporting distributed computing and deploying computation and storage resources close to the source’s physical location. Edge computing still is useful in handling advancing network difficulties like transporting massive data quantities in less time than traditional computing technologies, despite its distributed architecture, which can be demanding and necessitates ongoing control and monitoring.

The device layer, which comprises edge devices, the edge layer, which comprises of application and network layer, and the cloud layer are the three main layers of the edge that are identified by edge computing.

The location of where edge devices run on-premises is referred to as the device layer (cameras, sensors, controllers, industrial machines, etc.). These gadgets can gather and transmit data due to their computing power. A layer that supports apps and network workloads is known as a local edge layer. There are two layers to the local edge:

Application Layer — Due to its enormous footprint, the application layer which runs apps on endpoints cannot handle it.

Network Layer — The real or virtualized network layer operates routers and switches.

Edge data centers and Internet of Things (IoT) gateways are both parts of the edge layer. These operate on a local area network (LAN), which may be wireless, fiber-optic, 5G, or an earlier network like 4G. Individual gadgets, IoT devices, smartphones, tablets, and laptops that people carry are all visible in the edge layer and interact with the edge data center. A private area network, like RF or Bluetooth, is another method of device communication. On this layer, edge virtualization refers to the use of software representations of physical computer resources at a network’s edge, which is the area closest to the data-producing devices. Edge computing solutions must include virtualization. The deployment and operation of various apps on edge servers are made simpler by this technology.

A database that is maintained and configured for scalable business intelligence and analytics is known as a cloud data warehouse. The edge layer can be deployed on-premises or in the cloud despite the title. The cloud (or nexus) manages the processing that other edge nodes are unable to handle.

Why Edge Computing is Required?

The rapid growth of IoT devices, as well as their expanding computational capacity, has resulted in massive amounts of data. And as 5G networks expand the number of linked mobile devices, data volumes will keep rising. The goal of cloud and AI in the past was that they would automate and speed up innovation by generating actionable insights from the data. However, network and infrastructure capacities have been overtaken by the extraordinary amount and complexity of data provided by connected devices. Sending all of that data to a centralized data center or the cloud generates bandwidth and latency problems. Edge computing is a more efficient option since data is collected and analyzed near the point of origin. Latency is considerably decreased since data does not have to travel over a link to a cloud/data center to be processed.

Reduce the data amount sent to the cloud and stored there. The volume of data created at the edge is expanding at an exponentially faster rate than networks’ ability to process it. Endpoints should deliver data to an Edge Computing device that processes or analyses it instead of sending it to the cloud or a remote data center to complete the task.
Reduce the amount of time it takes for data to be transmitted and processed. Edge Computing significantly reduces the time required for data to be transmitted and processed, and the action that must be taken at the end. Because most of the raw data does not need to be transmitted up to the Cloud to be analyzed and interpreted, analysis and event processing may be done more rapidly and cost-efficiently. Cloud data centers can be hundreds, if not thousands, of kilometers away from an enabled device, resulting in tens to hundreds of milliseconds of round-trip delay. For robotic surgery, self-driving cars, and precision manufacturing, this level of latency is a lifetime. The cycle can be reduced to a few milliseconds using edge computing.
Reducing the signal-to-noise ratio is a good idea. Finally, Edge Computing helps firms prioritize data by reducing the signal-to-noise ratio, allowing them to focus on vital data that has to be evaluated, saved, and processed right away. Take, for example, the management of a refrigeration and air conditioning machine. Machine-generated data predominates in the data obtained, which is dominated by “I’m OK” telemetry state data. The system will occasionally emit an “I’m not OK” event, which is what the tracking firm is interested in. Everything else is “noise” data that obliterate the signal event. Edge computing aids in the prioritization of data that requires attention.

Edge computing’s distinct architecture tries to address three major network issues: latency, bandwidth, & network congestion.

· Latency

It represents the time it takes for a data packet to travel from one network point to another. Wider geographic distances and network congestion can cause latency, which slows down server response time. Lower latency aids in the creation of a better user experience, however, the distance between the client (user) making the request and also the server responding to the request is a challenge.

By getting the processing closer to the data source, the physical distance between the server and the user is reduced, allowing for faster response times.

· Bandwidth

It is the quantity of data carried by a network over time, measured in bits per second. It is restricted to all networks, particularly those used for wireless communication. As a result, only a small number of connected devices can share data in the network. You may have to pay more if you wish to increase your bandwidth. Controlling bandwidth utilization across a network connecting a wide range of devices is equally tough.

Edge computing is a solution to this issue. Because all computing takes place locally or at the data source, like computers, webcams, and other devices, bandwidth is only provided for their use, reducing waste.

· Congestion

The internet is made up of billions of devices all over the world exchanging data. This can be too much for the network, resulting in significant network congestion and processing times. Furthermore, network disruptions might occur, increasing congestion and disrupting interactions between users.

Edge computing allows numerous devices to function across a more productive and smaller LAN with nearby devices generating data using the available bandwidth by providing servers and data storage systems at or near the site where the data is generated. This drastically reduces congestion and latency.

Differences Between Edge Computing and Cloud Computing

First and foremost, it’s critical to recognize that cloud and edge computing are distinct, non-interchangeable technologies that can’t be replaced.

Applications of Edge Computing

Edge computing is used in a variety of industries. It gathers, processes, filters, and analyses data nearby or at the network edge. It’s used in a variety of places, including:

· IoT (Internet-of-Things) Devices

It’s a widespread misunderstanding that edge computing and the Internet of Things are the same things. In actuality, edge computing is a type of architecture, while IoT is a type of technology that makes use of it. Mobile phones, smart automobiles, smart thermostats, smart locks, smartwatches, and other smart gadgets access the internet and take advantage of code that runs on the device itself rather than the cloud for efficient use.

· Network Optimization

Edge computing aids network optimization by measuring and evaluating network performance for users throughout the web. It finds the least latency and most reliable network path for user traffic. It can also help to remove traffic congestion for better performance.

· Retail Industry

Retailers benefit from edge computing since it extends the life of their stores. It strengthens and improves the processes and services of retail stores. Edge computing offers real-time data analysis at the data source itself by allowing data gathering at the edge. As a result, make it simple for retailers to leverage big data and AI-based revolutionary solutions. It offers them insights at their site that assist them in creating the highest level of operational efficiency.

· Healthcare Industry

Edge computing enhances the way the health industry runs by increasing efficiency, reliability, and patient output. Consider the following scenario: A person in serious condition travels from house to hospital in an ambulance. In that situation, sending patients’ medical data to the cloud is quite challenging. Edge AI and computation can aid in the processing and analysis of data on the fly, as well as the implementation of recommended actions.

· Automobile Industry

The use of Edge AI in the car has yielded encouraging outcomes. A self-driving automobile is a simple illustration. All of the choices are made behind closed doors. From the speed of the vehicle to accident risk, steering wheel handling, engine health analysis, and battery health communication, everything is covered. AI is capable of recognizing potentially risky circumstances. To avoid a collision, it can inform the driver or take immediate control of the vehicle. Blind-spot detection, emergency braking, driver-assist steering, and cross-traffic detectors can all help prevent accidents and save lives.

Check-engine lights, low battery, and oil light warnings aren’t the only things connected vehicles can do. Hundreds of sensors are monitored by AI, which can detect faults before they impair vehicle performance. By monitoring hundreds of data points each second, AI cn detect impending component failure before it happens.

Future of Edge Computing

Edge computing’s future will improve in tandem with sophisticated networks such as 5G and satellite mesh, as well as artificial intelligence. The globe is suddenly opened up to some potentially futuristic possibilities by having greater capacity and power, easier access to fast and extensive networks (satellite, 5G), and smarter computers (AI).

From 2021 to 2028, the worldwide edge computing industry is predicted to develop at a compound annual growth rate (CAGR) of 38.4 percent. Edge computing adds a layer of complexity to the organization by requiring a diverse set of stakeholders to handle IT infrastructure, traffic delivery, connectivity, application development, and service management. Edge computing also combines hardware, software solutions, and networking architecture to suit a wide range of use cases across various industries. Edge computing is projected to generate significant growth potential for newcomers soon, as the technology has been in the initial phases of development and its deployment and operational models are still to mature.

Edge computing offered by 5G will benefit every industry significantly. It brings processing and data storage closer to the point where data is generated, allowing for improved data control and cost savings, as well as faster insights and actions and continuous operations. In fact, by 2025, half of all company data will be processed at the edge, up from ten percent currently.