Multi-stage resource-aware congestion control algorithm in edge computing environment

Abstract

Due to the uneven distribution of network resources and network, traffic, congestion is an inherent attribute of the Internet network. Network congestion will increase data transmission delay, reduce network throughput, increase the packet loss rate in the network, and even cause overall network performance degradation, which may cause large losses to users. This article aims to study multistage resource-aware congestion control algorithms based on edge computing environments. This article first compares and analyzes edge computing and traditional cloud computing. There are certain differences between edge computing technology and traditional cloud computing models. In the context of the Internet of Everything, the traditional cloud computing model also has some drawbacks. The low latency of edge computing can provide a good application experience and enhance the responsiveness of user applications. At the same time, the communication load of each network department is also reduced correspondingly, which solves the network congestion. Then, a congestion control algorithm based on cooperative paths was proposed, and the wireless sensor network topology and the limit of the congestion threshold of the wireless sensor network were introduced. Then it analyzes the problem of the MPTCP-based congestion control algorithm and analyzes the simulation results based on the energy-efficiency congestion control algorithm. The experimental results show that, compared with MPTCP, the throughput of the EECCA algorithm is increased by 52.6%, and it has better throughput performance. At the same time, the energy efficiency of the three algorithms in the simulation time is 1.1813 Mbits/J, 0.8311 Mbits/J, and 0.8021 Mbits/J, respectively. Compared with MPTCP, the energy efficiency of EECCA is increased by 47.3%, achieving the goal of energy saving.