Application-Signal Congestion Prediction for Low-Latency Networks
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Solution Overview
Problem
Current congestion control mechanisms in network traffic management are reactive, leading to latency and performance degradation, especially in modern traffic patterns with bursty or heterogeneous data flows, and are inadequate for latency-sensitive applications like distributed machine learning and video streaming.
Innovation Solution
Implement predictive congestion management (PCN) using application signals to anticipate future traffic loads by receiving signaling packets from packet sources, allowing network elements to take proactive actions such as ECN marking and traffic throttling before congestion occurs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive congestion control mechanisms are used, then network stability is maintained through traditional packet drop detection, but latency increases and performance degrades due to late response
Solution Approach 1:
The patent applies preliminary action by having packet sources send signaling packets in advance to network elements before actual data transmission occurs. This allows the network element to receive and process congestion control signals ahead of time, enabling proactive congestion management rather than reactive response. The signaling packets carry information about future data flows, allowing the network to prepare appropriate responses before congestion actually occurs.
2Productivity
If traditional reactive congestion control is implemented, then network elements can respond to congestion signals, but packet loss and network delays increase significantly before control mechanisms can react
Solution Approach 1:
The system implements preliminary action by receiving signaling packets that indicate future data flows before they actually occur. The network element processes these advance signals and prepares congestion control responses in advance, so when the actual data transmission happens, the control mechanisms are already in place to prevent packet loss rather than reacting after losses have occurred.
Solution Approach 2:
The patent employs feedback mechanisms where packet sources continuously send signaling packets to network elements about their intended data flows. This creates a feedback loop where the network element receives real-time information about upcoming traffic patterns and adjusts its congestion control parameters accordingly, enabling dynamic and adaptive congestion management that prevents packet loss before it occurs.
3Ease of operation
If existing transport protocols estimate network state to adjust transmission rates, then some congestion control is achieved, but latency increases and queue buildup occurs on switches
Solution Approach 1:
The patent applies preliminary action by having packet sources send signaling packets in advance of actual data transmission. This allows the network element to receive and process congestion control signals before the data flows occur, eliminating the latency inherent in traditional transport protocols that must wait for congestion to manifest before adjusting transmission rates.
Solution Approach 2:
The patent introduces signaling packets as an intermediary mechanism between packet sources and network elements. These signaling packets carry information about future data flows and serve as a mediator that enables the network element to anticipate and prepare for incoming traffic, rather than reacting after congestion has occurred. This intermediary layer facilitates proactive congestion control without the latency of traditional reactive approaches.
Data Source
AI summary
Embodiments of the present application provide systems, apparatus and methods for predictive congestion management using signals from packet sources. According to a method, a network element predicts future traffic loads by receiving signals from multiple packet sources that indicate the size and timing of incoming data flows. By analysing these signals, the network element forecasts potential traffic surges. Before the predicted traffic arrives, the network element takes preventive actions to manage the data load. By addressing potential overloads in advance, the method may allow for smooth and efficient network operation, maintaining stability and preventing congestion.