Analysis of Socket Communication Technology Based on Machine Learning Algorithms Under TCP/IP Protocol in Network Virtual Laboratory System

Abstract

When communicating between node machines in different locations in the network virtual lab system, the network layer shields the differences of the lower layer networks and cannot provide uniform data transmission of connectionless packets. Aiming at this problem, a self-correcting and optimal scheduling technique for communication networks based on deep machine learning algorithm is proposed. The branching algorithm of the algorithm mainly involves enhancing data learning. By using TCP/IP protocol for communication, a method and program implementation of communication using Socket mechanism under TCP/IP protocol are proposed. System users access the virtual lab primarily through the appropriate browser. The core part of the network virtual lab is server-side communication technology and experimental design. In a virtual lab system, a large number of nodes must be relied on for real-time communication, and the TCP/IP protocol must be followed. The Socket mechanism should be used to implement TCP/IP-based communication. Two breakthroughs were realized, resource scheduling decision and optimization of indirect system parameter selection. The experimental results show that the technical requirements of high throughput and ultra-low latency of the current virtual experimental system are realized.