Network QoS Optimization via Centralized Scheduling Engine
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Solution Overview
Problem
Existing network technologies face challenges in achieving consistent and optimal quality of service (QoS) across different network nodes due to varying QoS scheduling techniques, leading to sub-optimal performance and unpredictable throughput.
Innovation Solution
A system-wide scheduling engine (SWSE) is introduced to act as a central controller, coordinating QoS schedulers across network nodes by generating and adjusting scheduling techniques and tunable parameters based on key performance indicators (KPIs) to ensure consistent and optimal network performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If different network devices employ various QoS scheduling techniques independently, then each device can optimize its local performance, but the overall network QoS becomes inconsistent and unpredictable
Solution Approach 1:
The system implements feedback loops where network performance indicators (KPIs) are continuously monitored and fed back to the scheduling engine. The scheduling engine uses this feedback to dynamically adjust scheduling parameters and techniques across network devices, ensuring consistent QoS while maintaining optimal throughput. This closed-loop control resolves the contradiction by coordinating local optimizations with global QoS consistency.
Solution Approach 2:
The scheduling engine serves as a universal coordinating component that manages QoS across multiple different network devices and vendors. It provides a unified scheduling framework that can adapt to various device types and implementations, ensuring consistent QoS behavior throughout the heterogeneous network infrastructure while allowing each device to maintain its local optimization capabilities.
2Reliability
If a central scheduling controller is introduced to coordinate QoS across network devices, then QoS consistency is improved, but system complexity increases
Solution Approach 1:
The scheduling engine acts as an intermediary component between the network core and individual network devices. It abstracts the complexity of coordinated QoS management into standardized interfaces and protocols, allowing consistent QoS control without requiring complex modifications to each network device. This intermediary approach manages system complexity by centralizing the coordination logic in a dedicated component.
Solution Approach 2:
The system manages complexity by dynamically adjusting scheduling parameters rather than fundamentally changing the architecture of each network device. The scheduling engine modifies tunable parameters and configuration settings of existing QoS schedulers across devices, achieving consistent QoS through parameter optimization rather than structural complexity.
3Reliability
If QoS scheduling techniques are standardized across all network devices, then QoS consistency is improved, but adaptability to different vendor implementations is reduced
Solution Approach 1:
The scheduling engine applies the principle of local quality by allowing each network device to maintain its own scheduling implementation details and vendor-specific optimizations while coordinating through a common framework. Each device can have locally optimized scheduling parameters adapted to its specific capabilities, while the overall QoS consistency is achieved through the central scheduling engine's coordination and parameter management.
Data Source
AI summary
A system may be configured to receive information regarding a quality of service (“QoS”) objective for a network. The network may include a group of nodes through which network traffic traverses. Each node, of the group of nodes, may implement one or more queues that indicate an order in which traffic is processed by the node. The system may further identify scheduling information associated with one or more nodes of the network. The queues implemented by the one or more nodes may be based on the identified scheduling information. The system may receive performance information from at least one of the nodes, of the group of nodes; and may generate new scheduling information for at least one node, of the group of nodes, based on the information regarding the QoS objective, the scheduling information associated with the one or more nodes, and the performance information.