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

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 per...

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Main Authors: Xiang Xiao, Ming Zhao, Yusen Zhu
Format: Article
Language:English
Published: Elsevier 2022-11-01
Series:Energy Reports
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352484722008423
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author Xiang Xiao
Ming Zhao
Yusen Zhu
author_facet Xiang Xiao
Ming Zhao
Yusen Zhu
author_sort Xiang Xiao
collection DOAJ
description 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.
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spelling doaj.art-021fcbeab95e41d6be095a05a5d982462023-02-21T05:11:30ZengElsevierEnergy Reports2352-48472022-11-01863216331Multi-stage resource-aware congestion control algorithm in edge computing environmentXiang Xiao0Ming Zhao1Yusen Zhu2School of Computer Science and Engineering, Central South University, Changsha 410000, Hunan, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410000, Hunan, China; Corresponding author.School of Mathematics, Hunan University, Changsha 410000, Hunan, ChinaDue 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.http://www.sciencedirect.com/science/article/pii/S2352484722008423Edge computingMulti-stage resource awarenessCongestion control algorithmWireless sensor network
spellingShingle Xiang Xiao
Ming Zhao
Yusen Zhu
Multi-stage resource-aware congestion control algorithm in edge computing environment
Energy Reports
Edge computing
Multi-stage resource awareness
Congestion control algorithm
Wireless sensor network
title Multi-stage resource-aware congestion control algorithm in edge computing environment
title_full Multi-stage resource-aware congestion control algorithm in edge computing environment
title_fullStr Multi-stage resource-aware congestion control algorithm in edge computing environment
title_full_unstemmed Multi-stage resource-aware congestion control algorithm in edge computing environment
title_short Multi-stage resource-aware congestion control algorithm in edge computing environment
title_sort multi stage resource aware congestion control algorithm in edge computing environment
topic Edge computing
Multi-stage resource awareness
Congestion control algorithm
Wireless sensor network
url http://www.sciencedirect.com/science/article/pii/S2352484722008423
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AT mingzhao multistageresourceawarecongestioncontrolalgorithminedgecomputingenvironment
AT yusenzhu multistageresourceawarecongestioncontrolalgorithminedgecomputingenvironment