Edge computing is computing that takes place at or near the physical location of either the user or the source of the data being processed, such as a device or sensor. By placing computing services closer to these locations, users benefit from faster, more reliable services and organizations benefit from the flexibility and agility of the open hybrid cloud.
However, with the proliferation of devices and services at edge sites, there is an increasing amount to manage outside of the sphere of traditional operations. Platforms are being extended well beyond the data center, devices are multiplying and spreading across vast areas, and on-demand applications and services are running in significantly different and distant locations.
This evolving IT landscape is posing new challenges for organizations, including:
- Ensuring they have the skills to address evolving edge infrastructure requirements.
- Building capabilities that can react with minimal human interaction in a more secure and trusted way.
- Effectively scaling at the edge with an ever-increasing number of devices and endpoints to consider.
Of course, while there are difficult challenges to overcome, many of them can be mitigated with edge automation.
Edge automation benefits
By automating operations at the edge, organizations can reduce much of the complexity that comes from extending hybrid cloud infrastructure so that they are better able to take advantage of the benefits edge computing provides.
Edge automation can help organizations:
- Increase scalability by applying configurations more consistently across your infrastructure and managing edge devices more efficiently.
- Boost agility by adapting to changing customer demands and using edge resources only as needed.
- Focus on remote operational security and safety by running updates, patches, and required maintenance automatically without sending a technician to the site.
- Reduce downtime by simplifying network management and reducing the chance of human error.
- Improve efficiency by increasing performance with automated analysis, monitoring, and alerting.
Here are some government-specific use cases and examples that demonstrate the value of edge automation.
Transportation
By automating complex manual device configuration processes, transportation companies can efficiently deploy software and application updates to trains, airplanes, and other moving vehicles with significantly less human intervention. This can save time and help eliminate manual configuration errors, freeing teams to work on more strategic, innovative, and valuable projects.
Compared to a manual approach, automating device installation and management is generally safer and more reliable.
Industry 4.0 and Manufacturing
From oil and gas refineries to smart factories to supply chains, Industry 4.0 is seeing the integration of technologies such as the internet of things (IoT), cloud computing, analytics, and artificial intelligence/machine learning (AI/ML) into industrial production facilities and across operations.
One example of the value of edge automation in Industry 4.0 can be found on the manufacturing floor. There, supported by visualization algorithms, edge automation can help detect defects in manufactured components on the assembly line. It can also help improve the safety of factory operations by identifying and alerting hazardous conditions or unpermitted actions.
Smart cities
To improve services while increasing efficiency, many municipalities are incorporating edge technologies such as IoT and AI/ML to monitor and respond to issues affecting public safety, citizen satisfaction and environmental sustainability.
Early smart city projects were constrained by the technology of the time, but the rollout of 5G networks (and new communications technologies still to come) not only increase data speeds but also makes it possible to connect more devices. To scale capabilities more effectively, smart cities need to automate edge operations, including data collection, processing, monitoring, and alerting.
Healthcare
Healthcare has long since started to move away from hospitals toward remote care treatment options such as outpatient centers, clinics, and freestanding emergency rooms, and technologies have evolved and proliferated to support these new environments. Clinical decision-making can also be improved and personalized based on patient data generated from wearables and a variety of other medical devices.
Using automation, edge computing, and analytics, clinicians can efficiently convert this flood of new data into valuable insights to help improve patient outcomes while delivering both financial and operational value.
