Hierarchical Router Packet Queuing Under Cloud QoS Constraints
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Solution Overview
Problem
Existing technologies struggle to implement quality-of-service policies efficiently within cloud computing platforms for cellular service providers, as they lack control over the physical infrastructure and face arbitrary logical constraints, leading to challenges in prioritizing and managing different classes of telecommunication traffic.
Innovation Solution
A hierarchical quality-of-service policy is implemented within a cloud computing platform, comprising a parent sub-policy and a child sub-policy, which classifies and manages telecommunication traffic into different classes, ensuring priority is maintained even when physical constraints are unknown, by intelligently dropping packets that violate logical constraints while respecting overall bandwidth limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quality-of-service policies are implemented within a cloud computing platform, then telecommunication traffic can be prioritized and managed efficiently, but the cloud platform's arbitrary logical constraints and lack of control over physical infrastructure create management challenges
Solution Approach 1:
The quality-of-service policy is divided into a hierarchical structure with a parent policy and child policies. The parent policy handles overall bandwidth management and logical constraints, while child policies manage specific traffic classes. This segmentation allows independent optimization of each policy level without interfering with the other, resolving the complexity of managing both cloud constraints and traffic prioritization simultaneously.
Solution Approach 2:
The patent introduces an intermediary mechanism that sits between the cloud computing platform's logical constraints and the telecommunication traffic. This intermediary translates cloud platform bandwidth limits into actionable quality-of-service parameters, bridging the gap between the cloud's arbitrary constraints and the cellular network's traffic management requirements.
2Reliability
If telecommunication traffic is classified into different classes for priority management, then high-priority traffic like voice can be ensured, but the system faces challenges in adhering to unknown physical constraints and logical bandwidth limits
Solution Approach 1:
The quality-of-service policy is designed to be dynamic, automatically adjusting traffic class allocations and bandwidth distributions based on current cloud platform logical constraints. When constraints change, the system dynamically recalibrates the child policies to maintain high-priority traffic guarantees while adapting to new bandwidth limitations, eliminating the need for manual reconfiguration.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor bandwidth usage against cloud platform logical constraints. This feedback loop allows the hierarchical policy to detect when high-priority traffic approaches constraint limits and automatically adjust lower-priority traffic allocations, ensuring constraint compliance while maintaining service quality.
Data Source
AI summary
A disclosed method may include (i) implementing, within a cloud computing platform, a hierarchical quality-of-service policy that includes a parent quality-of-service sub-policy in relation to a child quality-of-service sub-policy, (ii) receiving telecommunication traffic as part of a cellular service platform implemented through the cloud computing platform, and (iii) applying, within the cloud computing platform, the hierarchical quality-of-service policy to the telecommunication traffic to satisfy a predetermined level of quality of service.