Coarse-Grained Load Balancing with Traffic-Aware Marking in Data Center Networks. (19th October 2022)

• Record Type: Journal Article
• Authors: Huang, Ran; Zhang, Jingliang; Hu, Yuanzhen; Zou, Shaojun; Luan, Xidao; Hu, Jinbin; Ruan, Chang; Zhang, Tao
• Other Names: Babaie Shahram Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-10-19
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/9594517
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : In the data center environment, meeting the dissimilar requirements of transmission performance for long and short data flows is crucial for guaranteeing the data center service quality. It is well known that optimizing the network transmission performance is the key. Abundant research studies have reported that designing an effective load-balancing scheme can boost the network performance by enhancing the path utilization of multirooted data center networks. Unfortunately, the existing data center load-balancing schemes fail to provide satisfying flow-level transmission performance since they choose to ignore the constant changing path status and blindly assign paths to data flows regardless of their different performance requirements. In order to solve these issues, we propose a data center load-balancing scheme called TAM to meet the performance requirements of long and short flows simultaneously. In detail, TAM leverages the traffic-aware ECN marking mechanism to force long flows to proactively maintain the switch queue length at a low level and release bandwidth to short flows when they compete for the bottleneck link. On the contrary, TAM carries out the coarse-grained routing or rerouting for short and long flows, respectively, in real time by perceiving the path status. In this way, the short flows achieve low delay transmission while the long flows obtain high throughput. The simulation results based on NS2 tests show that TAM achieves up to 60% lower FCTs for short flows and a 40% improvement in goodput for long flows.