Edge computing is a distributed computing paradigm that moves computer storage and processing to the edge of the network, where it’s closest to users and devices, more secure and, most critically, as close as possible to applications and data sources.
In a traditional computing model, data is sent to a centralized data center or a cloud provider for storage and processing. However, this can result in latency and bandwidth constraints.
With edge computing, devices and sensors located at the edge of the network can process and analyze data in real time. Because data doesn’t have to be sent to a central location for processing, organizations can process it locally, which can improve cost savings, response times and application performance.
What are the benefits of edge computing?
Putting computing at the edge allows organizations to improve how they manage their physical assets and create new, interactive human experiences. Some of the benefits of edge computing include:
- Reduced latency: Bringing data processing closer to the data source means you gain the time it would take to transmit data back and forth to the cloud. This allows for real-time processing and faster decision-making.
- Improved data security: Sensitive data doesn’t have to be sent to cloud servers, where it can be susceptible to cyberattacks or interception. By processing data locally, edge devices can ensure that sensitive data remains within the local network, enhancing data security.
- Cost-effective: Edge computing reduces the bandwidth and storage costs associated with transmitting data to the cloud, making it a more cost-effective option for organizations with large amounts of data.
- Scalability: With edge computing, organizations can scale their compute and storage resources dynamically, based on their needs. This makes it easier to handle any spikes in data volume and processing requirements.
- Enhanced reliability: Edge computing can improve the reliability of applications by enabling them to operate independently of the cloud. This offers advantages in scenarios where a network connection could be unstable or unreliable, such as at remote industrial sites or at offshore oil rigs.
Edge-computing uses cases and examples
Edge computing has a range of potential use cases, especially in applications where real-time data processing and low latency are critical. Some of the most common ones include:
- Industrial automation: Edge computing can enable real-time data processing and analysis for industrial automation applications such as predictive maintenance, machine learning and monitoring factory equipment.
- IoT devices: Edge computing can be used to process and analyze data locally, reducing latency and improving response times. This is particularly useful for applications that require real-time data processing, such as smart cities (for example, how NTT is helping to digitally transform the City of Las Vegas), home automation and wearable devices.
- Autonomous vehicles: Edge computing can enable self-driving vehicles to process data from sensors and cameras in real time, allowing them to make rapid decisions and respond to changes in their environment.
- Healthcare: Healthcare applications such as remote patient monitoring, real-time data analysis and predictive analytics can benefit greatly from the speed that edge computing enables.
- Retail and hospitality: Edge computing can enable real-time data processing and analysis for applications such as inventory management, customer analytics and personalized marketing.
- Video and media streaming: Finally, organizations can use edge computing for content delivery networks (CDNs) to improve streaming performance and reduce latency by caching and processing data at the edge of the network.
Challenges related to edge devices and ways to address them
While edge computing offers several benefits, it does come with a few challenges. They include:
- Network connectivity: Edge devices and sensors may be located in remote or hard-to-reach areas, which can make it difficult to maintain a reliable network connection.
- Security: Edge devices have to be secured against cyberthreats such as hacking or data breaches, which can compromise sensitive data.
- Scalability: As the number of edge devices and sensors grows, it can be difficult to manage and scale the infrastructure needed to support them.
- Data management: Edge devices generate large amounts of data, which can be difficult to manage and store effectively.
To address these challenges, there are several strategies that can be employed:
- Build a robust network infrastructure: A fast, reliable and scalable network infrastructure – for example, NTT’s Private 5G – is key to ensuring that edge devices can connect and communicate effectively.
- Establish security protocols: Implementing security protocols such as encryption, authentication and access control can help protect sensitive data.
- Use the right platforms: Platforms that provide tools for data management, analytics and application development can help to simplify the deployment and management of edge computing.
- Get expert support: Going beyond the technology itself, your organization may lack the broader expertise and support you need to make edge computing a success. Solutions such as NTT’s Edge as a Service can accelerate your digital transformation efforts in this regard in a scalable and cost-effective way.
- Cloud integration: Integrating edge computing with cloud services can help to provide additional processing power, storage and analytics capabilities.
What is the future of edge computing?
The future of edge computing looks promising, and an increased adoption of edge computing is expected in various sectors, including agriculture, transportation and logistics. As edge computing becomes more prevalent, there will be a growing demand for specialized hardware, such as edge servers and sensors, to support its infrastructure.
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The rollout of 5G networks is expected to further enhance the power of edge computing, providing ultrafast speeds and low latency for data processing and transmission. Edge computing is also a natural fit for AI applications, as it can provide real-time data processing and analysis for AI models. This is expected to further the growth of edge AI.
As more edge computing systems are deployed, there will be a need for standardization and interoperability to ensure that different systems can seamlessly communicate.
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