Learning-Based and Data-Driven TCP Design for Memory-Constrained IoT
| Autor: | Fan Zhou, Kaushik R. Chowdhury, Waleed Meleis, Wei Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
TCP acceleration
Transmission Control Protocol Computer science business.industry Distributed computing TCP tuning 020206 networking & telecommunications 02 engineering and technology Network topology Function approximation Transport layer 0202 electrical engineering electronic engineering information engineering Zeta-TCP 020201 artificial intelligence & image processing business Wireless sensor network Computer network |
| Zdroj: | DCOSS |
| DOI: | 10.1109/dcoss.2016.8 |
| Popis: | Advances in wireless technology have resulted in pervasive deployment of devices of a high variability in form factors, memory and computational ability. The need for maintaining continuous connections that deliver data with high reliability necessitate re-thinking of conventional design of the transport layer protocol. This paper investigates the use of Q-learning in TCP cwnd adaptation during the congestion avoidance state, wherein the classical alternation of the window is replaced, thereby allowing the protocol to immediately respond to previously seen network conditions. Furthermore, it demonstrates how memory plays a critical role in building the exploration space, and proposes ways to reduce this overhead through function approximation. The superior performance of the learning-based approach over TCP New Reno is demonstrated through a comprehensive simulation study, revealing 33.8% and 12.1% improvement in throughput and delay, respectively, for the evaluated topologies. We also show how function approximation can be used to dramatically reduce the memory requirements of a learning-based protocol while maintaining the same throughput and delay. |
| Databáze: | OpenAIRE |
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