- Edge computing is what it seems on the surface – computing on the edge
- Businesses adopting edge computing are poised to save resources and increase performance
- Edge computing is gaining attention for increased performance, but it also opens a business up to cyberattacks
It seems like there’s always a buzzword that accompanies computer technology innovations when they’re first introduced. One prominent example is cloud computing – a less expensive and more efficient model of operation for businesses.
When it all started there was just One Big Computer – the ENIAC. Then, during the Unix period, we discovered how to use simple (not derogatory) connectors to integrate with the ENIAC. Personal computers came next, marking the first time that ordinary folk really possessed the equipment that performed the job. And on and on, fast and furious, with the advancements since then.
Now we have edge computing. Is this just another buzzword? What does it mean for your business? Let’s find out.
What Is Edge Computing?
The term “edge” refers to a precise geographic location of operation. Edge computing performs computation right from your data source rather than depending on the cloud from the data centers to manipulate information and streamline processes.
Note that edge computing doesn’t eliminate the cloud – it complements it. How so?
Early edge computing projects intended to reduce the cost of bandwidth necessary to move unprocessed data from the place of generation to either a commercial data center or the cloud. The concept has evolved more recently due to the development of real-time programs that demand less latency, such as autonomous vehicles.
Edge computing is, in fact, closely related to the current global implementation of the 5G wireless standard due to 5G’s ability to speed up processing for these cutting-edge, low-latency use cases and applications.
What Edge Computing Looks Like in Process
The basic idea behind the edge’s hardware design is that client devices link to nearby edge modules for quicker performance and more seamless interactions. Edge devices might include IoT sensors, a worker’s laptop or mobile, security cameras, or even the internet-connected microwave in the break room.
An automated robot is an example of an edge device in an industrial setting, which could take the shape of advanced surgical technology in the medical profession that enables doctors to operate remotely. Edge gateways are edge devices in and of themselves and are part of an edge computing architecture. Nomenclature varies, so you might hear the modules referred to as edge servers or edge gateways.
Many edge gateways and servers will be installed by service providers (like Verizon for its 5G network) to allow edge networks, but companies looking to set up a private edge system will also need to consider this technology.
How is this relevant to your business?
Benefits of Edge Computing
Businesses can reinvent interactions with the help of edge and cloud. Manufacturing and IoT are only a few possible uses for edge computing.
Edge can be used to encourage quick decisions and enhance user experiences by boosting relevance at every touchpoint. Now, with the support of the broader cloud foundation, edge computing is assisting in creating new data and experiences.
These are all essential issues to consider, so let’s see how they’re relevant in the business setting.
It Saves Costs
Edge computing reduces the need for dedicated servers and bandwidth. Why? It’s simple: Cloud technologies and connectivity are limited and expensive. As smart cameras, scanners, heaters, and even toasters become standard in every home and workplace, Statista projects that by 2025, more than 75 billion IoT devices will be deployed globally. It will be necessary to relocate a sizable portion of computing to the edge if we’re to support all those gadgets.
It Enhances System Performance
The most significant benefit of edge computing is arguably its ability to evaluate and retain data more rapidly. Edge computing’s implementation adopts a more practical and meaningful approach, enabling more successful real-world applications crucial to organizations.
A smartphone reading a person’s face for picture identification historically would have to run the image recognition algorithm using a cloud-based program, which would take a lot of time. The algorithm might run remotely in an edge-computing architecture, utilizing an edge server, entry point, or even a smartphone.
Applications like virtual and augmented reality, self-driving cars, and even smart cities require this kind of rapid processing and response.
Edge computing uses the growing capacity of in-device computing to quickly give in-depth analysis and predictions. Improved analytics in edge devices can spur innovation to increase value and improve quality.
Additionally, it presents crucial strategic issues like:
- How should applications that carry out these kinds of operations be deployed in the context of additional computational capacity?
- How can you improve operational procedures for your staff, clients, and business through the usage of embedded intelligence in devices?
Large amounts of processing must travel to the edge to maximize the capabilities of all these devices.
Potential Issues of Edge Computing
Edge computing has the disadvantage of potentially increasing cyberattack avenues. Here’s what that means. There is additional potential for malevolent attackers to infiltrate your systems as more “smart” machines enter the mix, including edge servers and IoT devices with powerful built-in processors.
Edge computing also has the disadvantage of requiring additional local infrastructure. An IoT camera, for instance, needs a built-in machine to transmit its raw footage data to a server. To execute its kinematic algorithms, however, it would require a far more advanced computer with greater computing power. Hardware costs are falling, making the construction of smarter gadgets more affordable.
Contact the Experts at Techromatic
Edge computing isn’t a simple concept to grasp – after all, it hasn’t been completely figured out yet. The benefits can be very appealing if you know how to integrate edge into your business, and the best part is that you don’t have to go at it on your own.
The experts at Techromatic are always ready to help! We help businesses create, deploy, and manage processes like edge computing so they can boost productivity and efficiency.
Get in touch with Techromatic today to learn how to transform your business with edge computing.
Edge computing is a distributed computing architecture in which data processing and storage are moved closer to the end user. Edge computing enables applications to run faster by reducing the amount of time it takes for data to travel from the device to the cloud for processing. An example of edge computing is using sensors in autonomous vehicles to process data without having to send that data back to a centralized server for analysis.
- Reduced latency: By moving data processing and storage closer to the end user, the amount of time it takes for data to travel from the device to a server for analysis is reduced, resulting
in lower latency.
- Improved scalability: Edge computing offers improved scalability since it relies on distributed processing of data at multiple locations instead of having all data processed in one place.
- Increased security: Data can remain safe and secure at various nodes near the end user instead of having all sensitive information stored in a centralized location, which reduces the risk of security breaches or data loss.
- Cost savings: Edge computing can help reduce costs associated with maintaining physical infrastructure and power consumption as well as costly cloud services fees due to fewer virtual machines being used for processing.
- Accessibility: With edge computing, remote locations that may not have access to a reliable internet connection can still participate in data processing and storage activities due to its distributed architecture.
Edge computing is typically implemented in popular programming languages such as Java, Python, and C++. These languages are well suited for edge computing because of their ability to quickly process data and develop complex algorithms. Machine learning and artificial intelligence (AI) frameworks such as TensorFlow can also be used in edge computing applications.