In today’s digital age, technology is a fast-growing field. Every day introduces new advancements in the technological world and almost everyone is now interested in where technology will lead us in the future.
Edge computing is a new concept that develops a new obsession with a network-based system that connects numerous critical portions of a cloud system. It is an idea that attempts to integrate virtual data with the actual world, which is a significant technological advantage in providing firms with real-time services for speedier decision-making and production, but how did it begin? Let’s have a look into the history of Cloud Computing and how it shaped what we call today, Edge Computing.
Cloud Computing: How Did it All Start?
In the 1960s, researchers implemented a new concept that enables access to computing resources through a remote platform using a supercomputer. This idea was introduced during the mainframe era, which was a period in which compromises of centralized computing were developed and controlled by programmers and system operators.
As time went by, the idea advanced to the distributed computing paradigms. It is a way to distribute data using the send/receive method.
Thanks to multi-tier models and virtualization technology, these paradigms were popularized and evolved accordingly. Cloud computing started to popularize in the early 2000s. This is when top companies such as Amazon, Google, and Salesforce started designing web-based applications. The idea of cloud computing went viral as companies realized that accessing resources from anywhere is a very important asset for their business without investing in any physical infrastructure.
Several businesses nowadays use various cloud computing services. Despite the fact that cloud computing is more prevalent than ever, some companies are still hesitant to store sensitive data in the cloud due to privacy concerns. This spawned the present era of edge computing, which is a solution to these problems.
Edge Intelligence: Complementing Cloud Services
Edge computing is a distributed computing technology that processes data and runs software applications close to the data source. It is more concerned with local data processing than with centralising data and transmitting it to the cloud. The goal of edge computing is to reduce idle time due to local processing and to reduce the quantity of data that must be transferred to a larger network..
Edge Computing: How it Works?
The infrastructure of edge computing is fully powered by the developments in 5G networks which sets up better and high-performance local networks.
Edge computing offers data processing and analysis on devices or servers close to the edge of the network such as edge servers, edge clusters, and embedded devices, hence the name “Edge Computing”. This creates an efficient method of processing data while using an efficient number of network resources. Using edge computing limits the volume of data that travels through the network to the cloud.
Why Edge Computing is Advantageous?
What makes edge computing useful is that it is a very important tool used for real-time data analysis, or if data is hard to be transmitted for processing to a centralized data centre due to its large volume. For example, it is used in many applications that need to implement intelligence functionalities locally that require real-time processing.
One of the most important aspects of edge computing is to reduce the attack risks on cloud applications because of the discussed limitation of the volume of transferred data from the data sources sent to the cloud. This protects the data from any kind of breach.
Edge Computing Applications
Edge Computing is mainly used in applications that benefit from real-time data processing locally, while not being interested in sending and saving data to the cloud.
- Autonomous and connected vehicles: These vehicles implement local intelligence functionalities that require real-time processing such as obstacle avoidance and automatic breaks for the safety of the vehicle and driver.
- Some industrial product control systems: The system is required to operate in real-time to ensure a high-quality production process.
- Smart security applications in urban settings: This system needs to identify security risks (e.g., real-time video analytics system) instead of wasting time processing large volumes of data from the camera in the cloud.
Edge computing is a paradigm that complements the use of cloud computing. It creates a safe field for distributed data processing, and it balances between the centralized cloud resources and the local data processing. Now, we can even notice applications using the combination of “cloud/edge” technology which utilizes both cloud and edge technology.
IoT’s Relationship with Edge Computing
Internet of Things (IoT) are devices that are digitally connected devices that contain embedded internet connection. Their use in edge computing is vital because they sometimes serve as edge devices in the cloud/edge computing paradigms. IoT devices are used to transmit and process data while being small with low power consumption. The infrastructure for the analysis, processing, and storage of data is provided by cloud/edge technology while the IoT devices provide large amounts of data.
IoT allows new forms of edge intelligence provided by the device itself. It is now possible to use Artificial Intelligence (AI) models inside resource-constrained devices (e.g., microcontrollers) which enables the use of edge technology with Embedded Machine learning and TinyML.
How Cloud/Edge Computing Works with Internet of Things
After being introduced to all the layers of the cloud/edge/IoT computing paradigm, it’s time to explain how everything fits together.
- Cloud computing takes the top spot of the continuum providing a scalable infrastructure for processing and storing large amounts of data making it accessible on the cloud or multiple clouds (hybrid cloud).
- Edge computing takes the middle spot of the continuum making use of the local aspect and letting the storing service get closer to the IoT devices
- IoT devices are at the bottom of the continuum that offers a large range of network-connected devices that generate and transmit data. These devices include sensors, smart appliances, and wearables for industrial machinery.
The goal of these layers is to allow an efficient network and path for data transmission from the IoT devices.
Types of Edge Computing Infrastructures
There are many ways to deploy the distributed computing architectures based on the edge nodes, i.e., the different IoT devices, and the cloud infrastructure. According to the system, different models of edge computing can be used.
- Fog Computing: In this paradigm, data is processed, and storage is done using edge devices instead of the centralized cloud. The devices are called fog nodes. It is used in autonomous vehicles, smart cities edge computing, and the IoT industry where low latency is needed.
- Federated Learning: It is used in machine learning allowing many parties to train a model without sharing their data. Each party trains a model locally, and then a combination of all models creates a global model in the end. It improves scalability while reducing communication costs but at the cost of the model’s accuracy. It is found in mobile devices, healthcare, and finance.
- Swarm Learning: By combining federated learning with swarm intelligence, multiple parties can train a model in a decentralized fashion enhancing privacy and conversion speed at the cost of higher complexity and reduced scalability. It is used in robotics and smart grid deployment.
- Cloudlets: They are small-scale devices used for edge computing. They offer the usual edge computing advantages such as low latency and increased privacy, but at the cost of a less scalable system. Examples of cloudlets are IoT, mobile devices, smart homes, Augmented Reality (AR), and Virtual Reality (VR).
- Function-as-a-Service (FaaS): It is a cloud computing model that allows edge configurations. Developers can write software functions applied using web requests or events. FaaS grants reduced infrastructure costs, improved scalability, and fault tolerance, but have high complexity and reduced control over the execution. FaaS is used in chatbots for various industries.
- Mist Computing: It is like fog computing but has a smaller scale. The computing resources are closer to the edge of the network than fog computing allowing lower latency and better performance. It is used in applications with strict real-time requirements such as real-time control and defect detection.
- Satellite Edge Computing: Depending on the power of satellites, it gives computing resources at the network edge. Data processing and storage are done on the satellite or at a satellite ground station for faster response time and real-time use. It is used for disaster response and military operations.
- Hybrid Cloud/Edge Computing: It gives both cloud and edge computing advantages for the system combining the scalability and flexibility of the cloud and the low latency and high performance of edge computing. It is used in IoT edge computing analysis and video streaming as they require both centralized and decentralized data processing.
- Specialized Edge Computing Paradigms: Some systems require custom edge paradigms for special cases, such as blockchain edge. It uses the security offered by blockchain with the low latency and performance boost from edge computing. It can be used in supply chain management as it requires secure and transparent data processing.
Technology is changing at such a rapid pace that no one can foresee what will happen in the future. Edge computing is a valuable tool in practically any technological discipline. It displayed a considerable improvement in real-time-oriented systems that require some data processing and storage when combined with cloud technologies and IoT. Overall, edge computing will continue to have an impact on the technological world today and will have a secure future. Cogent provides a concise glimpse of the IT world. Follow us to stay up to date on the latest technical advances.