Edge Computing: Enhancing Performance in Web Applications

Edge Computing: Enhancing Performance and Efficiency in Web Applications 

Edge computing signifies a profound shift in the way we handle data from the growing number of connected devices in our environment. 

The Internet of Things is expanding at an exponential rate, and new applications that demand real-time processing power are emerging. the origin of a phenomenon that has recently drawn the attention of major research and analysis firms, like IDC, and is described as a mesh network of micro data centers with the ability to locally process and store vital data, and then transmit them to a central data center. 

Edge computing systems allow, also thanks to faster network technologies, not least 5G wireless, to accelerate the creation of real-time applications, from video processing to real-time analytics, from self-driving cars to artificial intelligence or robotics. 

Let's try to understand why edge computing is becoming increasingly important in enhancing performance and efficiency in web applications! 

What Is Edge Computing and How Does It Work? 

Initially conceived as a tool to reduce bandwidth costs associated with data transport, edge technology has found its true scope of application in the need to manage applications in real-time. 

Gartner defines it as part of a distributed topology, in which the processing component is located close to the point where objects or people produce and use information. 

We are talking about a distributed and decentralized IT architecture that therefore does not rely on a central system, perhaps hundreds of kilometers away, for the management of the collected data, and which therefore allows us to avoid latency problems that could harm the performance of an application, while also reducing the amount of data that must be processed centrally or in the cloud. 

Edge computing is also well suited to scenarios in which the coverage of fixed and mobile networks is limited and therefore connectivity between the center and the periphery and vice versa does not guarantee adequate performance. 

In the production sector, however, we think of the connected machines present on a production line: it is one thing to collect data from a single source, a single connected object; it's another - quite another - to deal with a significant number of objects that transmit data simultaneously. It's not just a problem of connectivity or latency: there is a problem of bandwidth costs that can become huge.  

Hardware and services effectively become a local source of processing and storage: an edge gateway, for example, can process data from an edge device and send only relevant data to cloud storage, reducing bandwidth needs. bandwidth, or can send them to the starting device in case of real-time application needs. 

Edge computing therefore represents a gateway through which virtually any industrial machine can connect and create a complete processing continuum starting from devices at the periphery of the system, up to the cloud, allowing “cloud-like” analysis and computing for running on the machine itself. 

Therefore, to understand how an edge computer works, we must think of it as a computational system that processes data coming from sensors and connected objects, far from centralized nodes and close to the logical edge of the network. 

The IT architecture of edge computing 

As Gartner underlined in its report, precisely in consideration of the growing diffusion of IoT and Industrial Internet of Things, it is time for companies to start using infrastructural architectures based on edge computing paradigms, therefore a distributed IT network architecture that enables mobile computing for locally produced data. Instead of sending data to cloud data centers, edge computing decentralizes processing power to ensure real-time processing without latency, while reducing bandwidth and storage requirements on the network. 
It is appropriate to think of the ideal infrastructure for edge computing as a mini datacenter, in fact the optimal technological choice for managing the IT needs of peripheral environments, characterized not only by particular flexibility, but also by the ability to support a wide variety of applications: the mini datacenter is therefore an answer, or perhaps "the" answer, in all those situations where rapid and easily repeatable implementations are needed, and above all where local processing is needed without necessarily having to provide specific local IT support. By mini or micro datacenters, we mean pre-configured, pre-integrated and pre-tested solutions complete with computing capacity, software, monitoring and security tools, power and protection. 

As for how to implement Edge computing, there are actually two possible options: implement and manage the entire edge computing stack in existing IT environments, relying on existing infrastructure and using services such as Microsoft Azure IoT which allows you to extend intelligence and processing capacity on devices installed in your local IT environment, or choose an 'edge cloud' managed and maintained by a public cloud provider, such as Amazon Web Services' AWS Lambda@Edge service, which allows you to run your code application in an AWS location close to the end user. 
It is no coincidence that Cisco recently announced a series of innovations for the Data Center world, starting with the expansion of ACI (Application Centric Infrastructure) in the cloud with AWS and Microsoft Azure environments, or the extension of Hyperflex in branches and in remote locations to power applications at the edge, as well as Cloud Center extensions to enable customers to manage application lifecycle across multiple cloud environments. 

Differences between cloud and edge computing 

A mistake not to be made when talking about edge computing is to consider it an alternative or even opposed technology to the cloud. 
In reality, precisely by virtue of the differences between cloud and edge computing, we are in the presence of two technologies that are complementary to each other. 
When talking about digital transformation or IoT or IIoT applications, one of the central issues is the analysis and discussion of where the applications will be hosted. A cloud approach is considered a safe option, if only for the fact that companies certainly know more about it. The idea is that it is sufficient to relocate, with the peace of mind of being able to exploit the unlimited hardware and software capacity of cloud environments. 
In reality, each of the two approaches has strengths and weaknesses that are important to take into proper consideration. 
Edge computing represents the best option in all those cases where: 
– the bandwidth is not sufficient or the quality of the network itself is not adequate to support sending data to the cloud. 
– the company has a particularly high focus on security and privacy issues and is therefore afraid of transmitting data through public networks or storing it in the cloud. 
– The communication network connection 
to the cloud is not strong enough to be reliable. – Applications require rapid data sampling or need to calculate results with minimal delay. 
Conversely, the cloud may be a better option in cases where: 
– Particularly high processing power is needed and there is a need to implement additional analysis tools at any time. 
– The form factor and environmental limitations of some applications negatively impact the costs of edge computing, effectively making the cloud more convenient. 
– The data sets are of particularly significant size . Having a large number of applications in the cloud and being able to acquire further data is one of the starting conditions for enabling machine learning or self-learning activities, which are particularly useful for perfecting the quality of the results. 
– When you need data to be distributed and displayed across a variety of platforms and devices. 

Benefits of Edge Computing for Business 

As we have repeatedly underlined, edge computing optimizes a company's data-driven activities, bringing the data collection, processing, and reporting phases as close as possible to end users. It therefore finds applications in enterprise-class data centers, in building management, in the healthcare sector, in smart cities, and in the world of manufacturing. 
There are 5 advantages of edge computing in companies. 
Let's see them together. 
1. Speed ​​and latency 

The longer it takes to process the data, the less relevant the result is. Let's think for example of the autonomous vehicle segment, considered one of the applications of excellence of edge computing: time is of the essence and most of the data that is collected and requested becomes useless after just a few milliseconds. Likewise, even in an industrial production plant, instant data analyzes are needed to highlight faults or situations of potential danger for the machinery, the product and even the human operator. Only by eliminating possible latencies or delays due to low network performance will the data remain relevant, useful and "actionable". 
An additional benefit related to the edge is that this technology reduces overall traffic loads, improving the performance of all applications and services. 
2. Security 
When all data must be sent to a cloud analyzer, it is undeniable that vulnerabilities related to critical business and operational processes increase. A single DDoS attack can disrupt all operations: when you distribute data analysis tools, you also distribute risk. With edge computing, it is true that the potential attack surface expands, but conversely the impact on the entire organization decreases. Another inherent truth is that the less data you transfer, the less data you can intercept. When data is analyzed locally, it remains protected by the local company's security blanket. At the same time, and this is an undeniable advantage, edge computing can overcome the issues of local compliance and privacy regulations, as well as the issue of data sovereignty. 
3. Savings 
Since not all data is the same and does not have the same value, it is not justified to spend the same amount to transport, manage and protect it. Some data is fundamental to the operation of a business, others are almost expendable. Edge computing allows you to classify data from a management perspective. By keeping the majority of your data at the edge, you reduce the need for bandwidth, which has a direct effect on reducing costs. 
All this while keeping in mind that moving to the edge does not mean doing without the cloud, but optimizing the data flow in order to maximize operating costs. 
4. Reliability 
When we talk about IoT, we are not always talking about optimal operating environments from the point of view of Internet connectivity. Overall system reliability can be improved when edge devices can store and process data locally. So let's go back to talking about micro data centers, capable of functioning in practically any environment, without temporary interruptions in connectivity having a negative impact on operations. 
5. Scalability 
Although it may seem counterintuitive, edge computing also offers advantages in terms of scalability, especially to avoid requiring expansions or changes to central cloud data centers. 

To Wrap Things Up  

The emergence of edge computing signifies an evolution in the architecture and management of distributed systems. Edge computing, which minimizes latency and optimizes data security, is expected to play a major role in the development of interconnected technology, spurring efficiency and innovation in a range of industries.