Network Traffic Scheduler with Compression Tunnels
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Solution Overview
Problem
Existing network traffic management systems face challenges in link oversubscription, bandwidth guarantee, and compression effects in complex network topologies, particularly in ensuring fair allocation and efficient transmission of data across multiple links and classes of traffic.
Innovation Solution
A scheduler system that selects sources and types of traffic based on link characteristics, guarantees minimum bandwidth, burst limits, and service intervals, and uses tunnels for compression and encryption, managing bandwidth through a radix tree algorithm and feedback loops to account for actual data usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If class based queuing (CBQ) or scheduling systems are used to manage network traffic, then link level scheduling is achieved, but link oversubscription problems persist and bandwidth guarantee becomes difficult
Solution Approach 1:
The patent segments network traffic into multiple classes with different priority levels and bandwidth requirements. Each class is independently scheduled and managed, allowing the system to handle link oversubscription by prioritizing critical traffic classes while still providing minimum bandwidth guarantees to lower priority classes. This segmentation enables fine-grained control over bandwidth allocation in oversubscribed environments.
Solution Approach 2:
The system dynamically adjusts scheduling parameters such as bandwidth allocation ratios, priority weights, and queue lengths based on current network conditions and traffic demands. This allows the scheduler to adapt to link oversubscription scenarios by modifying parameters in real-time, ensuring that bandwidth guarantees are maintained even when total traffic exceeds link capacity.
2Productivity
If data compression is applied to reduce bandwidth usage, then transmission efficiency improves, but system modifications are required at both ends of the compressed link
Solution Approach 1:
The patent introduces compression tunnels as intermediary components that handle data compression and decompression. These tunnels act as mediators between the sender and receiver, encapsulating the compression logic within dedicated tunnel endpoints. This approach allows compression to be applied without requiring modifications to the core network infrastructure or end systems, as the tunnels transparently handle the compression overhead.
3Adaptability or versatility
If multiple classes of traffic are scheduled simultaneously, then comprehensive bandwidth management is achieved, but scheduling complexity increases
Solution Approach 1:
The scheduling system is segmented into multiple independent schedulers, each responsible for a specific traffic class or link. This modular architecture allows comprehensive bandwidth management across multiple classes while keeping each individual scheduler relatively simple. Each scheduler handles only its designated traffic class, reducing the complexity of any single scheduling decision while maintaining overall system versatility.
Solution Approach 2:
The patent implements dynamic scheduling where priority levels and bandwidth allocations can change based on current network conditions and traffic patterns. This dynamic approach allows the system to maintain comprehensive bandwidth management flexibility while reducing complexity by adapting to common scenarios rather than handling all possible cases with rigid rules.
4Adaptability or versatility
If compression tunnels are used to manage bandwidth, then selective compression is enabled, but accounting for actual bandwidth usage becomes more complex
Solution Approach 1:
The patent implements feedback mechanisms where the scheduler receives information about actual bandwidth usage from compression tunnels and adjusts its decisions accordingly. This feedback loop allows the system to account for compression effects by monitoring actual transmitted bandwidth versus allocated bandwidth, enabling accurate chargeback and resource management despite the variable nature of compression ratios.
Data Source
AI summary
Data traffic is scheduled by, in a first scheduler, selecting a source of traffic from a plurality of sources of traffic, each source being associated with a second scheduler, in a second scheduler associated with the selected source of traffic, selecting a type of traffic from a plurality of types of traffic within the source selected by the first scheduler, and transmitting data of the selected type and source. Identifying and creating tunnels between endpoints in a network for optimum data traffic method and apparatus.