Predictive Scheduling for Network Packet Latency Reduction
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Solution Overview
Problem
Existing network interface devices face inefficiencies and latency due to the need to determine actual packet sizes before scheduling, which delays traffic control operations and does not scale well with certain communication protocols.
Innovation Solution
Implementing predictive scheduling techniques that use estimated packet sizes for initial scheduling, with subsequent correction factors applied based on actual packet sizes to adjust bandwidth allocation levels, allowing for efficient traffic control without prior knowledge of packet sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If actual packet size is used for scheduling, then bandwidth allocation accuracy is improved, but scheduling latency increases
Solution Approach 1:
The system performs preliminary scheduling actions using estimated packet sizes before the actual packet size is known. The QoS engine schedules packets based on size estimates from the network protocol, and only later retrieves the actual packet size to apply correction factors. This preliminary action eliminates scheduling latency while maintaining bandwidth allocation accuracy through subsequent corrections.
Solution Approach 2:
The system implements a feedback mechanism where the actual packet size is retrieved after initial scheduling and used to calculate correction factors. These correction factors are then applied to adjust the bandwidth allocation levels, ensuring that the initial scheduling decisions based on estimates are refined to match actual bandwidth consumption.
2Measurement precision
If actual packet size determination is performed before scheduling, then traffic control accuracy is improved, but processing efficiency deteriorates
Solution Approach 1:
The system performs scheduling as a preliminary action before actual packet size is determined. The QoS engine makes scheduling decisions based on estimated sizes, allowing the DMA engine to begin packet processing immediately without waiting for actual size determination, thus maintaining high processing efficiency.
Solution Approach 2:
After scheduling, the system retrieves the actual packet size and applies correction factors to the bandwidth allocation levels. This feedback loop ensures traffic control accuracy is maintained without delaying the scheduling decision, thereby preserving processing efficiency.
3Speed
If packet size estimation is used for scheduling, then scheduling speed is improved, but bandwidth allocation precision deteriorates
Solution Approach 1:
The system uses packet size estimation as a preliminary mechanism to enable fast scheduling decisions. The QoS engine quickly schedules packets based on estimated sizes from network protocols, achieving high scheduling speed without waiting for actual packet size determination.
Solution Approach 2:
To compensate for the imprecision of estimation, the system implements feedback by retrieving actual packet sizes and applying correction factors to bandwidth allocation levels. This ensures that while scheduling is fast, the final bandwidth allocation precision is maintained through the correction mechanism.
4Measurement precision
If correction factors are applied based on actual packet sizes, then bandwidth allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies correction factors as a feedback mechanism to improve bandwidth allocation accuracy. When actual packet sizes are determined, the system calculates the difference between estimated and actual sizes and adjusts bandwidth allocation levels accordingly. This feedback-based correction maintains accuracy without requiring complete redesign of the scheduling architecture.
Solution Approach 2:
The system modifies bandwidth allocation parameters dynamically by applying correction factors based on actual packet sizes. This parameter adjustment approach allows the system to maintain high bandwidth allocation accuracy while using relatively simple mechanisms that build upon the existing scheduling framework rather than requiring complex new systems.
Data Source
AI summary
Systems and methods are disclosed for enhancing network performance by using modified traffic control (e.g., rate limiting and/or scheduling) techniques to control a rate of packet (e.g., data packet) traffic to a queue scheduled by a Quality of Service (QoS) engine for reading and transmission. In particular, the QoS engine schedules packets using estimated packet sizes before an actual packet size is known by a direct memory access (DMA) engine coupled to the QoS engine. The QoS engine subsequently compensates for discrepancies between the estimated packet sizes and actual packet sizes (e.g., when the DMA engine has received an actual packet size of the scheduled packet). Using these modified traffic control techniques that leverage estimating packet sizes may reduce and/or eliminate latency introduced due to determining actual packet sizes.