Optimal Weighted Load Balancing in TCAMs

TL;DR

This paper presents efficient algorithms for approximating traffic load balancing in networks using limited TCAM rules, optimizing memory use while maintaining near-ideal traffic distribution.

Contribution

It introduces algorithms for selecting a limited number of TCAM rules to closely approximate desired traffic partitions, addressing practical constraints.

Findings

Algorithms effectively minimize overload error with limited rules.

Error increases with fewer rules, more servers, or narrower TCAMs.

Approximations are close to optimal for practical network configurations.

Abstract

Traffic splitting is a required functionality in networks, for example for load balancing over multiple paths or among different servers. The capacities of the servers determine the partition by which traffic should be split. A recent approach implements traffic splitting within the ternary content addressable memory (TCAM), which is often available in switches. It is important to reduce the amount of memory allocated for this task since TCAMs are power consuming and are often also required for other tasks such as classification and routing. Previous work showed how to compute the smallest prefix-matching TCAM necessary to implement a given partition exactly. In this paper we solve the more practical case, where at most $n$ prefix-matching TCAM rules are available, restricting the ability to implement exactly the desired partition. We give simple and efficient algorithms to find $n$ rules that generate a partition closest in $L_\infty$ to the desired one. We do the same for a one-sided version of $L_\infty$ which equals to the maximum overload on a server and for a relative version of it. We use our algorithms to evaluate how the expected error changes as a function of the number of rules, the number of servers, and the width of the TCAM.