Edge Computing Substrate Extensions for Network Latency Reduction
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Solution Overview
Problem
Cloud computing platforms face challenges in providing low latency computing resources to end users due to physical constraints such as network distance and number of hops between user devices and centralized data centers, limiting the achievement of very low latencies required for applications like game streaming, virtual reality, and autonomous vehicles.
Innovation Solution
Deploying provider substrate extensions within communications service provider networks, which are closer to end users, allowing for dramatically lower access latency by extending the cloud provider network edge locations into these networks, enabling low-latency interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computing resources are provisioned from centralized data centers, then resource pooling and management efficiency are improved, but network latency increases due to physical distance and number of hops
Solution Approach 1:
The patent segments the centralized cloud computing system into distributed edge computing nodes deployed within telecommunications networks. These edge locations are geographically distributed closer to end users, breaking the monolithic data center structure into multiple regional nodes that can serve local users with lower latency while maintaining centralized resource pooling capabilities through coordinated management.
Solution Approach 2:
The patent introduces a new spatial dimension by deploying computing resources within the telecommunications network infrastructure itself, rather than solely in centralized data centers. This creates a multi-tier architecture where edge locations are positioned in an intermediate dimension between centralized data centers and end users, reducing the physical distance and network hops required for data transmission.
2Loss of time
If cloud provider network edge locations are extended into communications service provider networks, then access latency is reduced, but network complexity and integration challenges increase
Solution Approach 1:
The patent introduces an intermediary layer consisting of edge locations that operate within the telecommunications network but are managed through standardized interfaces. These edge locations act as mediators between the centralized cloud control plane and the local telecommunications network infrastructure, abstracting away the complexity of network integration while enabling low-latency access to computing resources.
Solution Approach 2:
The patent designs the edge location architecture to provide multiple functions through a unified platform: local compute resources, storage capacity, networking capabilities, and seamless integration with both centralized cloud services and local telecommunications networks. This multi-functional approach reduces the need for separate specialized systems, thereby reducing overall complexity.
3Speed
If computing resources are placed closer to end users, then responsiveness and application performance are improved, but infrastructure deployment complexity increases
Solution Approach 1:
The patent merges cloud computing resources with existing telecommunications network infrastructure, utilizing the same physical locations and network pathways that carriers already operate. By co-locating edge computing nodes within existing carrier facilities and leveraging their network infrastructure, the patent avoids the need for separate dedicated infrastructure deployments, thereby reducing deployment complexity while maintaining proximity to end users.
Data Source
AI summary
Techniques for API-based endpoint discovery involving provider substrate extension resources are described. A discovery coordinator service located within the provider network can identify one or more endpoints from a set of potentially distributed endpoints for a client to utilize, where endpoints may be located within provider substrate extensions of the provider network. The discovery coordinator service can utilize location values of the client provided via an API request, such as its network address or geographic coordinates, to identify a nearby resource that may be most optimal for the client to use via providing minimal latency of access.