The boom in internet-connected devices and the collection of massive data has led businesses to combat the challenges of storing, processing, and securing data at the required size. Though companies are implementing digital plans, the enormous volumes of data find applicability for edge computing.
The processing power is moved to the edge network in edge computing instead of moving the data to the centralized server, either the cloud provider or data center. Edge computing analyzes data close to the location where it is collected, reducing the internet bandwidth usage and addressing the scalability and security challenges at data storage.
Enterprises have opted to move their real-time applications to the cloud over the past few years, as they have known the significance of cloud architecture. Analysts suggest that about 70% of the organizations have a minimum of one application on the cloud, and the key decision-makers have expressed attaining digital transformation as their key priority. However, the growing data has set limitations on the cloud strategy.
According to The IDC’s Data Age 2025 report, 175 trillion gigabytes will be generated globally by 2025, and above 90 zettabytes of this data will be created by edge devices. Hence, this data needs to be stored before processing it. Moreover, challenges exist to support the required bandwidth. Also, latency is one significant problem as information needs to travel to a specific location for processing and return to generate results.
Moreover, there is no guarantee that the network will be ever available or consistent. If the network is unavailable for a specific reason, applications remain offline. According to analysts, 91% of today’s data is generated and processed at centralized centers. By 2022, 75% of the data will require analysis and processing at the edge.
Edge for enterprises
Edge computing is not only about sensors and other internet-of-things (IoT) devices but also includes devices such as servers, laptops, etc. Enterprise resource planning (ERP), data management systems, financial software are the few enterprise applications that do not require real-time data processing.
Edge computing is highly relevant for enterprise software concerning application delivery. Employees only require sufficient data, instead of complete data or the complete application suite, to improve performance and user experience.
Edge computing enables utilizing AI applications in enterprise applications such as call centers, customer support, and voice recognition. Even though the relative algorithms are trained in the cloud, voice recognition applications require working locally for quick responses.
Latest applications and services need computing infrastructure that delivers high performance and low latency at the edge network. Public Infrastructure Network Node (PINN) meets this essential by focusing on AI at the edge. PINN supports intelligent transportation systems (ITS), edge computing, GPS, 5G wireless. PINN cluster has the potential to provide a vast amount of computing power without requiring any heavy cables and more cell towers.
PINN clusters can collect information from sensors and cameras at the street crossing. These devices can view things that a driver cannot perceive, such as changes in the traffic lights, pedestrian entry into zebra crossing, an emergency vehicle on the way. Edge computing using PINN is what enables all these to happen.
Best practices for edge development
Consistent usage of tools :
Developers must employ the same tools irrespective of where the application is deployed. As a result, special skills are not required for creating edge applications. Moreover, rapid application development and easy deployment on edge or the cloud
Open APIs enable accessing real-time data programmatically, allowing businesses to offer new services that were not practical before. Also, developers can use the public APIs to create solutions enabling data access without worrying about the hidden hardware interfaces.
Enhanced application development
Edge architecture is experiencing significant changes. However, the design decisions of the past will affect the future potential. Developers’ agility remains limited if the purpose of development is only for the edge. So, investing in technologies that can work anywhere- edge, cloud, and on-premises is worth investing in.
Loaded applications are easy to deploy and scalable. Significantly, such applications are suitable for edge computing requirements, including immutability, segregation, and modularity. Moreover, applications need to be deployed on various edge tiers, each carrying distinct resource characteristics.
As edge computing implementation increases, its advantages are experienced more. Instead of using edge computing as a separate computing model, the most preferred approach would be to use a hybrid computing model.
Developers building edge applications should optimize the next-generation application development processes, including our discussed development practices. So, it is time to maximize edge computing and experience its best benefits.