A surplus amount of data is being generated daily from businesses, enterprises, factories, hospitals, banks, and other established facilities. Therefore, it has become more critical to manage, store, and process data efficiently. It’s especially evident in the case of time-sensitive businesses to process data quickly and effectively for minimal safety risks and faster business operations. For this, Edge computing can help. But what is it all about? Isn’t the cloud enough? Let’s clear out these doubts by understanding Edge computing in detail.
What Is Edge Computing?
Edge computing is the modern, distributed computing architecture that brings data storage and computation closer to the data source. This helps save bandwidth and improve the response time. Simply put, edge computing involves fewer processes running in the cloud. It also moves those computing processes to edge devices, such as IoT devices, edge servers, or users’ computers. This way of bringing computation closer or at the network’s edge reduces long-distance communication between a server and a client. Therefore, it reduces bandwidth usage and latency. Edge computing is essentially an architecture instead of a technology per se. It is location-specific computing that doesn’t rely on the cloud to perform the work. However, it never means that the cloud won’t exist; it just becomes closer.
The Origin of Edge Computing
Edge computing originated as a concept in content delivery networks (CDNs) created in the 1990s to deliver video and web content using edge servers deployed closer to the users. In the 2000s, those networks evolved and started hosting apps and app components directly at the edge servers. This is how the first usage of edge computing appeared commercially. Eventually, edge computing solutions and services were developed to host apps such as shopping carts, data aggregation in real-time, ad insertion, and more.
Edge Computing Architecture
Computing tasks require a proper architecture. And there’s no “one size fits all” policy here. Different types of computing tasks need different architecture. Edge computing, over the years, has become an important architecture to support distributed computing and deploy storage and computation resources close to the same geographical location as the source. Although it employs decentralized architecture, which can be challenging and requires continuous control and monitoring, edge computing is still effective in solving advancing network issues like moving large data volumes in less time than other computing methods. The unique architecture of edge computing aims to solve three main network challenges – latency, bandwidth, and network congestion.
Latency
It refers to the time when a data packet goes from one point in the network to another. Lower latency helps build a more fabulous user experience, but its challenge is the distance between a user (client) making the request and the server attending the request. Latency can increase with larger geographical distances and network congestion, which delays the server response time. By placing the computation closer to the data source, you are actually reducing the physical distance between the server and the client to enable faster response times.
Bandwidth
It’s the amount of data a network carries over time and is measured in bits/second. It is limited to all networks, especially for wireless communications. Therefore, a limited number of devices can exchange data in a network. And if you want to increase this bandwidth, you might have to pay extra. Plus, controlling bandwidth usage is also difficult across the network connecting a large number of devices. Edge computing solves this problem. As all the computation happens close or at the source of data, such as computers, webcams, etc., bandwidth is supplied for their usage only, reducing wastage.
Congestion
The internet involves billions of devices exchanging data across the world. This can be overwhelming for the network and result in high network congestion and response delays. Additionally, network outages can also happen and increase the congestion more to disrupt communications between users. Deploying servers and data storage at or near the location where the data is generated, edge computing enables multiple devices to operate over a more efficient and smaller LAN where local devices generating data can use the available bandwidth. This way, it reduces congestion and latency significantly.
How Does Edge Computing Work?
The edge computing concept is not entirely new; it dates back to decades associated with remote computing. For example, branch offices and remote workplaces placed computing resources at a location where they can reap maximum benefits instead of relying on a central location. In traditional computing, where data was produced at the client-side (like a user’s PC), it moved across the internet to corporate LAN to store data and process it using an enterprise app. Next, the output is sent back, traveling through the internet, to reach the client’s device. Now, modern IT architects have moved from the concept of centralized data centers and embraced the edge infrastructure. Here, the computing and storage resources are moved from a data center to the location where the user generates the data (or the data source). This implies that you are bringing the data center close to the data source, not the other way around. It requires a partial gear rack that helps operate on a remote LAN and collects the data locally to process it. Some may deploy the gear in shielded enclosures to safeguard it from high temperature, humidity, moisture, and other climatic conditions. The edge computing process involves data normalization and analysis to find business intelligence, sending only the relevant data after analysis to the main data center. Furthermore, business intelligence here can mean:
Video surveillance in retail shops Sales data Predictive analytics for equipment repair and maintenance Power generation, Maintaining product quality, Ensure proper device functioning and more.
Advantages and Disadvantages
Advantages
The benefits of edge computing are as follows:
#1. Faster Response Times
Deploying computation processes at or near the edge devices helps reduce latency, as explained above. For example, suppose one employee wants to deliver some urgent message to another employee in the same company premises. It takes more time to send the message as it routes outside the building and communicates with a distant server located anywhere in the world and then comes back as a received message. With Edge computing, the router is the in-charge of data transfers within the office, significantly reducing delays. It also saves bandwidth to a great extent.
#2. Cost Efficiency
Edge computing helps save server resources and bandwidth, which in turn saves cost. If you deploy cloud resources to support a large number of devices at offices or homes with smart devices, the cost becomes higher. But edge computing can reduce this expenditure by moving the computation part of all these devices to the edge.
#3. Data Security and Privacy
Moving data across servers located internationally comes with privacy, security, and more legal issues. If it’s hijacked and falls into the wrong hands, it can cause deep concerns. Edge computing keeps data closer to its source, within the boundaries of data laws such as HIPAA and GDPR. It helps process data locally and avoid sensitive data to move to the cloud or a data center. Hence, your data remains safe within your premises. In addition, data going to the cloud or distant servers can also be encrypted by implementing edge computing. This way, data becomes more secure from cyberattacks.
#4. Easy Maintenance
Edge computing requires minimal effort and cost to maintain the edge devices and systems. It consumes less electricity for data processing, and cooling needs to keep the systems operating at the optimal performance is also lesser.
Disadvantages
The disadvantages of edge computing are:
#1. Limited Scope
Implementing edge computing could be effective, but its purpose and scope are limited. This is one of the reasons people are attracted to the cloud.
#2. Connectivity
Edge computing must have good connectivity to process data effectively. And if the connectivity is lost, it requires solid failure planning to overcome the issues that come along.
#3. Security Loopholes
With the increased usage of smart devices, the risk vector of attackers compromising the devices increases.
Applications of Edge Computing
Edge computing finds applications in various industries. It is used to aggregate, process, filter, and analyze data near or at the network edge. Some of the areas where it is applied are:
IoT Devices
It’s a common misconception that edge computing and IoT are the same. In reality, edge computing is an architecture, whereas IoT is a technology that uses edge computing. Smart devices like smartphones, smart thermostats, smart vehicles, smart locks, smartwatches, etc., connect to the internet and benefit from code running on those devices themselves instead of the cloud for efficient use.
Optimizing Network
Edge computing helps optimize the network by measuring and improving its performance across the web for users. It finds a network path with the lowest latency and most reliability for user traffic. In addition, it can also clear out traffic congestion for optimal performance.
Healthcare
A vast amount of data is generated from the healthcare industry. It involves patient data from medical equipment, sensors, and devices. Therefore, there is a greater need to manage, process, and store the data. Edge computing helps here by applying machine learning and automation for data access. It helps identify problematic data that requires immediate attention by clinicians to enable better patient care and eliminate health incidents. In addition, edge computing is used in medical monitoring systems to respond quickly in real-time instead of waiting for a cloud server to act.
Retail
Retail businesses also generate large chunks of data from stock tracking, sales, surveillance, and other business information. Using edge computing enables people to collect and analyze this data and find business opportunities like sales prediction, optimizing vendor orders, conducting effective campaigns, and more.
Manufacturing
Edge computing is used in the manufacturing sector to monitor manufacturing processes and apply machine learning and real-time analytics to improve product qualities and detect production errors. It also supports the environmental sensors to be incorporated in manufacturing plants. Furthermore, edge computing provides insights into the components in stock and how long they would go. It helps the manufacturer to make accurate and faster business decisions on operations and the factory.
Construction
The construction industry uses edge computing mainly for workplace safety to collect and analyze data taken from safety devices, cameras, sensors, etc. It helps businesses overview workplace safety conditions and ensures that employees are following safety protocols.
Transportation
The transportation sector, especially autonomous vehicles, produces terabytes of data every day. Autonomous vehicles need data to be collected and analyzed while they are moving, in real-time, which requires heavy computing. They also need data on vehicle condition, speed, location, road and traffic conditions, and nearby vehicles. To handle this, the vehicles themselves become the edge where the computing takes place. As a result, data is processed at an accelerated speed to fuel the data collection and analysis needs.
Agriculture
In farming, edge computing is utilized in sensors to track nutrient density and water usage and optimize the harvest. For this, the sensor collects data on environmental, temperature, and soil conditions. It analyzes their effects to help enhance the crop yield and ensure they are harvested during the most favorable environmental conditions.
Energy
Edge computing is useful in the energy sector as well to monitor safety with gas and oil utilities. Sensors monitor the humidity and pressure continuously. Additionally, it must not lose connectivity because if something wrong happens, like an overheating oil pipe goes undetected, it can lead to disasters. The challenge is that most of those facilities are situated in remote areas, where connectivity is poor. Hence, deploying edge computing at those systems or near them offers greater connectivity and continuous monitoring capabilities. Edge computing can also determine real-time equipment malfunctions. The sensors can monitor energy generated by all the machines such as electric vehicles, wind farm systems, and more with grid control to help in cost reduction and efficient energy generation. Other edge computing applications are for video conferencing that consumes large bandwidths, efficient caching with code running on CDN edge networks, financial services such as banks for security, and more.
Far Edge vs. Near Edge
Edge computing involves so many terms, such as near edge, far edge, etc., that it sometimes becomes confusing. Let’s understand the difference between the far edge and near edge.
Far Edge
It’s the infrastructure deployed farthest from a cloud datacenter while closest to the users. For instance, the Far Edge infrastructure for a mobile service agency can be near the base stations of cellphone towers. Far Edge computing is deployed at enterprises, factories, shopping malls, etc. The apps running on this infrastructure need high throughput, scalability, and low latency, which is great for video streaming, AR/VR, video gaming, etc. Based on hosted apps, it is known as:
An Enterprise Edge that hosts enterprise apps IoT Edge that hosts IoT apps
Near Edge
It’s the computing infrastructure deployed between the cloud data centers and the Far Edge. It hosts generic applications and services, unlike Far Edge that hosts specific apps. For instance, Near Edge infrastructure can be used for CDN caching, Fog computing, etc. Also, Fog computing puts storage and computer resources within or near the data, may not be at the data. It is a middle ground between a cloud data center located far away and the edge situated at the source with limited resources.
Edge Computing vs. Cloud Computing (Similarities and Differences)
Both Edge and Cloud computing involve distributed computing and deployment of storage and compute resources based on data being produced. However, they are definitely not the same. Here’s how they are different.
Deployment: Cloud computing deploys resources at global locations with high scalability to run processes. It can include centralized computing closer to the data source(s) but not at a network’s edge. On the other hand, edge computing deploys resources where the data is generated. Centralization/Decentralization: Using centralization, the cloud offers efficient and scalable resources with security and control. Edge computing is decentralized and used to address those concerns and use cases that are not provided in cloud computing’s centralization approach. Architecture: The cloud computing architecture consists of several loose-coupled components. It delivers apps and services on the pay-as-you-go model. However, edge computing extends above cloud computing and provides a more stable architecture. Programming: App development in the cloud is suitable and utilizes one or fewer programming languages. Edge computing may require different programming languages to develop apps. Response time: The average response time usually is more in cloud computing compared to edge computing. Hence, edge computing offers a faster computing process. Bandwidth: Cloud computing consumes more bandwidth and power due to the higher distance between the client and the server, while edge computing requires comparatively lower bandwidth and power.
What Are the Benefits of Edge Computing over Cloud Computing?
The process in edge computing is more efficient than cloud computing as the latter takes more time to fetch the data a user has requested. Cloud computing can delay information relay to a data center, which slows the decision-making process to cause latency. As a result, organizations may suffer losses in terms of cost, bandwidth, data safety, and even occupational hazards, especially in the case of manufacturing and construction. Here are a few benefits of the Edge over Cloud.
The demand for a faster, safer, and reliable architecture has popularized the growth of edge computing, making organizations choose edge computing over cloud computing. So, in the areas that need time-sensitive information, edge computing works wonders. When the computing process is carried out in remote locations, edge computing works better due to little to no connectivity to enable a centralized approach. It will help with local storage, working as a micro data center. Edge computing is a better solution for supporting smart and specialized devices that perform special functions and are different from regular devices. Edge computing can effectively address bandwidth usage, high cost, security, and power consumption in most areas compared to cloud computing.
Current Providers of Edge Computing
To deploy edge computing quickly and easily in your business or enterprise, you require an edge computing service provider. They help process the data and transmit it efficiently, offer a robust IT infrastructure, and manage massive data generated from the edge devices. Here are some of the notable edge computing providers: AWS offers consistent experience with a cloud-edge model and provides solutions and services for IoT, ML, AI, analytics, robotics, storage, and computation.
#2. Dell
Dell provides edge computing orchestration and management through OpenManage Mobile. Dell is great for digital cities, retailers, manufacturers, and others.
#3. ClearBlade
ClearBlade released their Edge Native Intelligent Asset Application that allows an edge maintainer to build alert devices and connect to IoT devices without coding. Other notable edge computing providers are Cloudflare, StackPath, Intel, EdgeConnex, and more.
Final Words 👩🏫
Edge computing can be an efficient, reliable, and cost-saving option for modern businesses that use digital services and solutions than ever before. It’s also an excellent concept to support the remote work culture to facilitate faster data processing and communication.
