Edge Security Acceleration (ESA) provides an efficient, flexible, and low-latency edge computing solution through Edge Routine, Edge Container, Edge KV, and AI Gateway.
How they work together
Build an efficient, flexible, and low-latency edge computing architecture for business needs of any scale and complexity.
Edge Routine: event-driven tasks with low compute power and simple business logic.
Edge Container: compute-intensive workloads with complex logic and high-performance computing.
Edge KV: data storage for edge workloads, enabling fast access and persistence.
AI Gateway: integrated monitoring and management platform for AI applications.
Data processing: Edge Routine handles lightweight event-driven tasks, while Edge Container handles resource-intensive complex logic.
Data storage: Edge KV stores intermediate results and persistent data for Edge Routine and Edge Container, improving access speed and reducing latency.
Data synchronization: Edge KV syncs data across cloud and edge nodes for consistency and reliability.
How it works
This solution integrates Edge Routine, Edge Container, and Edge KV into a flexible edge computing architecture.
Features
Feature | Description | |
Functions and Pages provides a rich set of performance and error monitoring metrics to help you understand your service workload and identify exceptions. | ||
Instant Logs is a lightweight and easy-to-use log service that allows you to view logs for Functions and Pages in real time in the console. You can use the data from Instant Logs to understand the execution status of your functions and make adjustments to Functions and Pages accordingly. | ||
The ESA CLI ( | ||
ESA provides ready-to-use function templates so you can deploy edge logic for common use cases without writing code from scratch. | ||
The APIs for Edge Function and Pages process requests on points of presence (POPs) closest to your users, reducing latency and delivering faster responses. | ||
Edge Container is a serverless compute service that runs container applications on a global network of points of presence (POPs). By deploying your applications closer to your users, Edge Container significantly reduces latency and simplifies protocol handling. It automatically manages scaling and operations, so you can focus on developing your application instead of managing infrastructure. | ||
Edge KV is a globally distributed key-value store that runs at points of presence (POPs). Writes propagate to all POPs automatically, and Edge Routine (ER) reads data from the same POP where the request lands, keeping latency low. Common use cases include lightweight Blockchain as a Service (BaaS) and API gateway services. | ||
AI Gateway is a unified API proxy service provided by ESA that forwards AI requests at the edge, adding observability, security, and performance optimization to your AI applications. | ||
Benefits
Low latency
Proximity-based computing: Processing and storing data on edge nodes reduces round trips to the central cloud.
Real-time response: Ideal for IoT device monitoring and real-time data analysis.
Cost-effective
Pay-as-you-go: Edge Routine bills by actual usage. Size Edge Container and Edge KV to match your needs.
Reduced bandwidth costs: Processing data at the edge reduces transfers to the central cloud and lowers network costs.
Flexibility and scalability
Multi-language support: Edge Container supports multiple programming languages and frameworks for complex applications.
Automatic scaling: Edge Routine and Edge Container scale automatically based on load.
Portability: Migrate containers across environments with ease.
High availability and security
Resource isolation: Edge Container provides independent runtime environments for isolation and security between applications.
Data synchronization: Edge KV keeps data consistent and reliable across nodes.
Use cases
IoT applications:
Real-time monitoring: Process sensor data and monitor device status with Edge Routine and Edge Container.
Data preprocessing: Preprocess sensor data at the edge to reduce cloud uploads.
Video stream processing:
Real-time transcoding: Transcode and process video streams with Edge Container.
Content delivery: Cache video content in Edge KV to reduce playback latency.
Big data analytics:
Real-time analytics: Generate instant reports and insights on edge nodes.
Batch processing: Process large datasets and run complex jobs with Edge Container.
Machine learning and AI:
Model inference: Run ML models on edge nodes for fast inference.
Data preprocessing: Preprocess data at the edge to prepare for model training.
Retail and logistics:
Inventory management: Process inventory data in real time with Edge Routine and Edge Container.
Supply chain monitoring: Monitor and optimize logistics workflows in real time at the edge.