Abstract:
This paper introduces the Dynamic Adaptive Packet Buffering (DAPB) algorithm. Designed to enhance data transfer efficiency in modern networking environments, it is built on the principles of Nagle's algorithm. DAPB addresses the limitations of existing buffering techniques by dynamically adjusting its behavior based on real-time network conditions, application requirements, and latency sensitivity. The algorithm incorporates context-sensitive buffering, adaptive timeout mechanisms, and machine learning-driven predictions to achieve a balance between efficiency, latency, and energy consumption. DAPB's context-aware buffering tailors its strategy to the specific needs of the application, minimizing buffering for latency-sensitive applications like VoIP and online gaming, while maximizing buffering for throughput-sensitive applications like file transfers. The adaptive timeout mechanism dynamically adjusts the waiting timeout based on network conditions such as round-trip time, packet loss, and jitter, ensuring optimal performance under varying workloads. Machine learning models are used to predict optimal buffer sizes and timeout values, leveraging historical data and real-time metrics to improve decision-making. The algorithm also features selective aggregation, intelligently deciding which packets to aggregate and which to send immediately. This ensures that urgent packets are transmitted without delay, while nonurgent packets are aggregated to reduce overhead. Additionally, DAPB prioritizes energy efficiency by optimizing buffer sizes and timeout values, making it suitable for energy-constrained environments like edge computing and IoT devices. The DAPB algorithm is expected to improve the data transfer performance in various scenarios. Compared to the standard Nagle algorithm, the DAPB algorithm is expected to reduce latency, improve throughput, and enhance energy efficiency. This paper is the result of a research project implemented as part of the Basic Research Program at the National Research University Higher School of Economics (HSE University).
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