Edge Computing in the Age of AI: An Overview | Dell Technologies Info Hub
Edge computing is a distributed computing paradigm that brings data processing and analysis closer to the source of data generation, rather than relying on centralized cloud or datacenter resources. It aims to address the limitations of traditional cloud computing, such as latency, bandwidth constraints, privacy concerns, and the need for real-time decision-making.
Edge computing finds applications across various industries, including manufacturing, transportation, healthcare, retail, agriculture, and digital cities. It empowers real-time monitoring, control, and optimization of processes. This enables efficient data analysis and decision-making at the edge as it complements cloud computing by providing a distributed computing infrastructure.
Here are some common examples:
- Industrial internet of things (IIoT)-Edge computing enables real-time monitoring, control, and optimization of industrial processes. It can be used for predictive maintenance, quality control, energy management, and overall operational efficiency improvements.
- Digital c ities-Edge computing supports the development of intelligent and connected urban environments. It can be utilized for traffic management, smart lighting, waste management, public safety, and environmental monitoring.
- Autonomous vehicles-Edge computing plays a vital role in autonomous vehicle technology. By processing sensor data locally, edge computing enables real-time decision-making, reducing reliance on cloud connectivity and ensuring quick response times for safe navigation.
- Healthcare-Edge computing helps in remote patient monitoring, telemedicine, and real-time health data analysis. It enables faster diagnosis, personalized treatment, and improved patient outcomes.
- Retail-Edge computing is used in retail for inventory management, personalized marketing, loss prevention, and in-store analytics. It enables real-time data processing for optimizing supply chains, improving customer experiences, and implementing dynamic pricing strategies.
- Energy management-Edge computing can be employed in smart grids to monitor energy consumption, optimize distribution, and detect anomalies. It enables efficient energy management, load balancing, and integration of renewable energy sources.
- Surveillance and security-Edge computing enhances video surveillance systems by enabling local video analysis, object recognition, and real-time threat detection. It reduces bandwidth requirements and enables faster response times for security incidents.
- Agriculture-Edge computing is utilized in precision farming for monitoring and optimizing crop conditions. It enables the analysis of sensor data related to soil moisture, weather conditions, and crop health, allowing farmers to make informed decisions regarding irrigation, fertilization, and pest control.
These are just a few examples, and the applications of edge computing continue to expand as technology advances. The key idea is to process data closer to its source, reducing latency, improving reliability, and enabling real-time decision-making for time-sensitive applications.
Edge computing brings numerous benefits, but it also presents a set of challenges that organizations need to address. The following image highlights some common challenges associated with edge computing:
The following diagram represents a typical edge computing architecture and its associated taxonomy.
A typical edge computing architecture consists of several components working together to enable data processing and analysis at the edge. Here are the key elements you would find in such an architecture:
- Edge devices-These are the devices deployed at the network edge, such as sensors, IoT devices, gateways, or edge servers. They collect and generate data from various sources and act as the first point of data processing.
- Edge gateway-An edge gateway is a device that acts as an intermediary between edge devices and the rest of the architecture. It aggregates and filters data from multiple devices, performs initial pre-processing, and ensures secure communication with other components.
- Edge computing infrastructure-This includes edge servers or edge nodes deployed at the edge locations. These servers have computational power, storage, and networking capabilities. They are responsible for running edge applications and processing data locally.
- Edge software stack-The edge software stack consists of various software components installed on edge devices and servers. It typically includes operating systems, containerization technologies (such as Docker or Kubernetes), and edge computing frameworks for deploying and managing edge applications.
- Edge analytics and AI-Edge analytics involves running data analysis and machine learning algorithms at the edge. This enables real-time insights and decision-making without relying on a centralized cloud infrastructure. Edge AI refers to the deployment of artificial intelligence algorithms and models at the edge for local inference and decision-making. The next section: Edge Inferencing describes the main use case in this regard.
- Connectivity-Edge computing architectures rely on connectivity technologies to transfer data between edge devices, edge servers, and other components. This can include wired and wireless networks, such as Ethernet, Wi-Fi, cellular networks, or even specialized protocols for IoT devices.
- Cloud or centralized infrastructure-While edge computing emphasizes local processing, there is often a connection to a centralized cloud or data center for certain tasks. This connection allows for remote management, data storage, more resource-intensive processing, or long-term analytics. Those resources are often broken down into two tiers — near and far edge:
- Far edge: Far edge refers to computing infrastructure and resources that are located close to the edge devices or sensors generating the data. It involves placing computational power and storage capabilities in proximity to where the data is produced. Far edge computing enables real-time or low-latency processing of data, reducing the need for transmitting all the data to a centralized cloud or datacenter.
- Near edge: Near edge, sometimes referred to as the “cloud edge” or “remote edge” describes computing infrastructure and resources that are positioned farther away from the edge devices. In the near edge model, data is typically collected and pre-processed at the edge, and then transmitted to a more centralized location, such as a cloud or datacenter for further analysis, storage, or long-term processing.
- Management and orchestration-To effectively manage the edge computing infrastructure, there is a need for centralized management and orchestration tools. These tools handle tasks like provisioning, monitoring, configuration management, software updates, and security management for the edge devices and servers.
It is important to note that while the components and the configurations of edge solution may differ, the overall objective remains the same: to process and analyze data at the edge to achieve real-time insights, reduced latency, improved efficiency, and better overall performance.
Edge Inferencing
Data growth driven by data intensive applications and ubiquitous sensors to enable real time insight is growing three times faster than access network. This drives data processing at the edge to keep up with the pace and reduce cloud cost and latency. IDC estimates that by 2027 62% of enterprises data will be processed at the edge!
Inference at the edge is a technique that enables data-gathering from devices to provide actionable intelligence using AI techniques rather than relying solely on cloud-based servers or data centers. It involves installing an edge server with an integrated AI accelerator (or a dedicated AI gateway device) close to the source of data, which results in much faster response time. 1This technique improves performance by reducing the time from input data to inference insight, and reduces the dependency on network connectivity, ultimately improving the business bottom line. 2Inference at the edge also improves security as the large dataset do not have to be transferred to the cloud. 3
Edge computing in the age of AI marks a significant paradigm shift in how data is processed, and insights are generated. By bringing AI to the edge, we can unlock real-time decision-making, improve efficiency, and enable innovations across various industries. While challenges exist, advancements in hardware, software, and security are paving the way for a future where intelligent edge devices are an integral part of our interconnected world.
It is expected that Inferencing market alone will overtake training with highest growth at the edge — necessitating competition in data center, near edge, and far edge.
For more information on how edge-inferencing works, refer to the next post on this regard: Inferencing at the Edge
1 https://steatite-embedded.co.uk/what-is-ai-inference-at-the-edge/
2 https://www.storagereview.com/review/edge-inferencing-is-getting-serious-thanks-to-new-hardware
Originally published at https://infohub.delltechnologies.com.