Cost-Aware Cloud Routing Optimization
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Solution Overview
Problem
Current network solutions and routing technologies do not effectively account for egress costs when determining the lowest cost path or paths with the best Service Level Agreement (SLA) guarantees, leading to increased costs for customers using public cloud provider services.
Innovation Solution
The implementation of systems, methods, and devices that autonomously optimize traffic routing decisions based on real-time egress costs, using an egress-cost-based pricing model to minimize total egress charges levied against customers of cloud computing networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional routing protocols are used to determine the shortest path, then routing simplicity is maintained, but egress costs increase due to lack of cost awareness
Solution Approach 1:
The patent modifies routing protocols by incorporating egress cost parameters into path selection algorithms. Routing decisions are enhanced to consider real-time egress cost data from multiple cloud providers, transforming traditional shortest-path routing into cost-aware routing that optimizes for both simplicity and cost efficiency
Solution Approach 2:
The system introduces an intermediary cost optimization layer that sits between traditional routing protocols and actual data transmission. This intermediary component collects egress cost information from multiple cloud providers, processes routing decisions based on cost parameters, and directs traffic through optimal paths without requiring changes to existing routing infrastructure
2Loss of energy
If egress cost optimization is implemented, then egress charges are reduced, but system complexity increases due to multi-cloud coordination
Solution Approach 1:
The patent creates a universal cost optimization platform that works across multiple cloud providers and routing protocols simultaneously. The system provides multi-functional capabilities including cost data collection, path optimization, and real-time routing decisions within a single integrated architecture, reducing the need for separate optimization systems for each cloud provider
Solution Approach 2:
The system implements self-service mechanisms where the cost optimization platform automatically collects egress cost data from cloud providers, performs routing optimization calculations, and adjusts traffic paths without requiring manual intervention. The system autonomously monitors cost changes and dynamically reconfigures routing decisions to maintain cost efficiency
3Measurement precision
If real-time cost monitoring is performed, then routing accuracy is improved, but computational overhead increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and caching egress cost data from cloud providers before routing decisions are needed. Cost information is gathered in advance and stored for quick retrieval, allowing routing optimizations to use pre-processed data rather than performing real-time cost queries, thereby reducing computational overhead while maintaining routing accuracy
Data Source
AI summary
Cost aware routing in a network topology to reduce costs within an egress-based pricing model. A method includes receiving telemetry data from one or more of a network device or a compute device within a cloud computing network, wherein the telemetry data is associated with a customer of the cloud computing network. The method includes retrieving an egress-based pricing scheme associated with a provider of the cloud computing network and provisioning one or more of the network device or the compute device to optimize routing decisions for the customer to reduce a predicted data egress charge for the customer. A route may be selected to traverse multiple clouds and/or colocation providers according to ingress, egress, and transfer charges. Segment routing and VRFs may be used to implement routes selected based on criteria such as cost, latency, throughput, and jitter.