Luopan : Sampling based Load Balancing in Data Center Networks

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

8 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)133-145
Number of pages13
Journal / PublicationIEEE Transactions on Parallel and Distributed Systems
Volume30
Issue number1
Online published23 Jul 2018
Publication statusPublished - Jan 2019

Abstract

Data center networks demand high-performance, robust, and practical data plane load balancing protocols. Despite progress, existing work falls short of meeting these requirements. We design, analyze, and evaluate Luopan, a novel sampling based load balancing protocol that overcomes these challenges. Luopan operates at flowcell granularity similar to Presto. It periodically samples a few paths for each destination switch and directs flowcells to the least congested one. By being congestion-aware, Luopan improves flow completion time (FCT), and is more robust to topological asymmetries compared to Presto. The sampling approach simplifies the protocol and makes it much more scalable for implementation in large-scale networks compared to existing congestion-aware schemes. We provide analysis to show that Luopan’s periodic sampling has the same asymptotic behavior as instantaneous sampling : taking 2 random samples provides exponential improvements over 1 sample. We conduct comprehensive packet-level simulations with production workloads. The results show that Luopan consistently outperforms state-of-the-art schemes in large-scale topologies. Compared to Presto, Luopan with 2 samples improves the 99.9%ile FCT of mice flows by up to 35%, and average FCT of medium and elephant flows by up to 30%. Luopan also performs significantly better than Local Sampling with large asymmetry.

Research Area(s)

  • Data center networks, Data centers, distributed, load balancing, Load management, Mice, network congestion, Production, Protocols, Switches, Topology