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SlimeMold: Hardware Load Balancer at Scale in Datacenter

Published: 05 September 2023 Publication History

Abstract

Stateful load balancers (LB) are essential services in cloud data centers, playing a crucial role in enhancing the availability and capacity of applications. Numerous studies have proposed methods to improve the throughput, connections per second, and concurrent flows of single LBs. For instance, with the advancement of programmable switches, hardware-based load balancers (HLB) have become mainstream due to their high efficiency. However, programmable switches still face the issue of limited registers and table entries, preventing them from fully meeting the performance requirements of data centers. In this paper, rather than solely focusing on enhancing individual HLBs, we introduce SlimeMold, which enables HLBs to work collaboratively at scale as an integrated LB system in data centers.
First, we design a novel HLB building block capable of achieving load balancing and exchanging states with other building blocks in the data plane. Next, we decouple forwarding and state operations, organizing the states using our proposed 2-level mapping mechanism. Finally, we optimize the system with flow caching and table entry balancing. We implement a real HLB building block using the Broadcom 56788 SmartToR chip, which attains line rate for state read and >1M OPS for flow write operations. Our simulation demonstrates full scalability in large-scale experiments, supporting 454 million concurrent flows with 512 state-hosting building blocks.

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      cover image ACM Other conferences
      APNet '23: Proceedings of the 7th Asia-Pacific Workshop on Networking
      June 2023
      229 pages
      ISBN:9798400707827
      DOI:10.1145/3600061
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 05 September 2023

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      Author Tags

      1. Load Balancing
      2. Programmable Switches

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      APNET 2023
      APNET 2023: 7th Asia-Pacific Workshop on Networking
      June 29 - 30, 2023
      Hong Kong, China

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