Resource Assignment Protocol for Edge-Compute Traffic Routing
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Solution Overview
Problem
Existing network infrastructure faces congestion issues during peak times, leading to reduced quality of service and user experience due to increased data traffic and latency, which current solutions like transparent caching do not adequately address.
Innovation Solution
Implementing a resource assignment protocol that directs client requests for content or processing to a dynamic edge-instance proximate to the client, reducing data transfer over the internet core and enhancing Quality of Service (QoS) by using a policy server to authorize and redirect traffic to edge-compute resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If network infrastructure is expanded to increase available bandwidth, then network congestion is reduced, but cost and complexity increase
Solution Approach 1:
The network is segmented into core network and edge network components. Edge-compute resources are deployed at strategic locations near clients, dividing the network into segments that handle different types of traffic. This segmentation allows traffic to be routed locally at the edge rather than traversing the entire core network, improving bandwidth efficiency without requiring expansion of the core infrastructure.
Solution Approach 2:
A resource assignment server acts as an intermediary between clients and content sources. It receives client requests, determines the optimal edge-compute resource location, and redirects traffic accordingly. This intermediary enables intelligent traffic routing that improves bandwidth utilization without requiring physical expansion of network infrastructure.
2Speed
If transparent caching is used to improve content delivery speed, then specific preplaced content is delivered faster, but traffic differentiation and dynamic content placement are not enabled
Solution Approach 1:
The system transitions from static preplaced content caching to dynamic edge-compute resource placement. Edge-compute resources can be dynamically instantiated, configured, and deactivated based on real-time traffic patterns and client needs. The resource assignment server dynamically determines optimal routing decisions, enabling both fast content delivery and adaptive traffic differentiation for different clients and content types.
Solution Approach 2:
Edge-compute resources serve multiple functions: they can cache content like traditional caches, perform dynamic content generation, execute application logic, and provide service-specific processing. This multi-functionality enables the system to handle diverse traffic types with differentiated QoS requirements while maintaining fast delivery speeds, overcoming the limitations of transparent caching.
3Quantity of substance
If more optical fiber and coaxial cable lines are added to reduce network congestion, then bandwidth capacity increases, but cost and redundancy during off-peak times increase
Solution Approach 1:
Edge-compute resources perform preliminary processing and content delivery actions closer to clients before traffic reaches the core network. By handling requests locally at the edge during peak times, the system prevents core network congestion without requiring additional core bandwidth capacity. This preliminary action at the edge optimizes bandwidth utilization efficiency across the entire network.
4Ease of operation
If client requests are routed through the internet core to distant remote content sources, then centralized resource management is maintained, but latency and network congestion increase
Solution Approach 1:
The system implements local quality by deploying edge-compute resources with specific functionalities at strategic locations near client groups. Each edge resource is optimized for local service delivery, reducing latency for nearby clients. The resource assignment server maintains centralized coordination by managing resource instantiation and routing decisions, combining local performance optimization with centralized resource management.
Data Source
AI summary
Aspects of the present disclosure provide a technical improvement to the problem of network congestion that exists when providing online services to clients. A system, method, and computer readable storage device use a resource assignment protocol to direct client requests for content and remote processing to a dynamic edge-compute instance placed in proximity to the client, thereby improving the Quality of Service QoS of network traffic. A policy server is implemented for serving specific clients with specific dynamically-placed services. Accordingly, traditional network traffic associated with communicating with a remote resource server and computational demand on the remote resource server are reduced, thereby making the network more efficient.