Adaptive Grant Service Upstream Latency Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
DOCSIS networks experience high latency due to the delay caused by the request-grant cycle, particularly in upstream data transmission, which is inefficient for latency-sensitive applications like gaming and video conferencing.
Innovation Solution
The implementation of an Adaptive Grant Service that dynamically adjusts the size of proactive upstream grants based on actual transmission bursts, optimizing bandwidth allocation and reducing latency by predicting and adapting to changing bandwidth needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional request-grant cycle is used in DOCSIS networks, then bandwidth allocation follows standard protocol, but upstream latency increases due to request-delay-grant cycle
Solution Approach 1:
The system performs preliminary actions by proactively granting upstream bandwidth allocations before cable modems actually need to transmit data. The MAC layer scheduler predicts future bandwidth needs and issues grants in advance, eliminating the request-delay-grant cycle and reducing upstream latency for time-sensitive applications.
Solution Approach 2:
The grant scheduling system dynamically adjusts bandwidth allocations based on real-time network conditions, application requirements, and predicted traffic patterns. The scheduler continuously modifies grant sizes, timing, and frequency to optimize performance for different service types while managing complexity through adaptive algorithms.
2Loss of time
If proactive grants are issued to reduce latency, then upstream latency decreases, but bandwidth efficiency deteriorates due to potential over-allocation
Solution Approach 1:
The system implements feedback mechanisms where the MAC layer scheduler continuously monitors actual bandwidth usage, transmission success rates, and network conditions. Based on this feedback, the scheduler adjusts proactive grant allocations to match actual needs, preventing over-allocation and maintaining high bandwidth efficiency while preserving low latency benefits.
Solution Approach 2:
The system dynamically changes grant parameters such as allocation size, timing, and frequency based on application requirements and network conditions. By adjusting these parameters in real-time, the system optimizes the balance between reducing latency through proactive grants and maintaining bandwidth efficiency through precise allocation matching actual traffic demands.
3Speed
If bandwidth grants are increased for latency-sensitive applications, then application performance improves, but overall network bandwidth utilization deteriorates
Solution Approach 1:
The system applies local quality by providing differentiated grant scheduling treatments to different service flows based on their specific requirements. Latency-sensitive applications receive proactive, optimized grants with higher priority and adjusted timing, while other traffic follows standard protocols. This localized optimization improves speed for critical applications without compromising overall network bandwidth utilization.
Solution Approach 2:
The grant scheduling system dynamically adjusts bandwidth allocations based on application requirements, network conditions, and traffic patterns. By continuously adapting grant parameters for different service flows, the system maintains high transmission speeds for latency-sensitive applications while optimizing overall network bandwidth utilization through flexible resource distribution.
Data Source
AI summary
Systems and methods that adaptively grant amounts of bandwidth to a remote device for upstream transmissions. The systems and methods may adaptively grant a first amount of bandwidth during a first interval, and vary the amount of bandwidth proactively granted over subsequent intervals using a metric of usage of the proactive bandwidth granted.