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All-Norm Load Balancing in Graph Streams via the Multiplicative Weights Update Method

Authors Sepehr Assadi, Aaron Bernstein, Zachary Langley

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Author Details

Sepehr Assadi
  • Rutgers University, New Brunswick, NJ, USA
Aaron Bernstein
  • Rutgers University, New Brunswick, NJ, USA
Zachary Langley
  • Rutgers University, New Brunswick, NJ, USA

Funding

  • Assadi, Sepehr: Research supported in part by NSF CAREER Award CCF-2047061, a Google Research gift, and a Fulcrum award from Rutgers Research Council.
  • Bernstein, Aaron: Research supported in part by NSF CAREER Award CCF-1942010.

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Sepehr Assadi, Aaron Bernstein, and Zachary Langley. All-Norm Load Balancing in Graph Streams via the Multiplicative Weights Update Method. In 14th Innovations in Theoretical Computer Science Conference (ITCS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 251, pp. 7:1-7:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.ITCS.2023.7

Abstract

In the weighted load balancing problem, the input is an n-vertex bipartite graph between a set of clients and a set of servers, and each client comes with some nonnegative real weight. The output is an assignment that maps each client to one of its adjacent servers, and the load of a server is then the sum of the weights of the clients assigned to it. The goal is to find an assignment that is well-balanced, typically captured by (approximately) minimizing either the 𝓁_∞- or 𝓁₂-norm of the server loads. Generalizing both of these objectives, the all-norm load balancing problem asks for an assignment that approximately minimizes all 𝓁_p-norm objectives for p ≥ 1, including p = ∞, simultaneously. Our main result is a deterministic O(log n)-pass O(1)-approximation semi-streaming algorithm for the all-norm load balancing problem. Prior to our work, only an O(log n)-pass O(log n)-approximation algorithm for the 𝓁_∞-norm objective was known in the semi-streaming setting. Our algorithm uses a novel application of the multiplicative weights update method to a mixed covering/packing convex program for the all-norm load balancing problem involving an infinite number of constraints.

Subject Classification

ACM Subject Classification
  • Theory of computation → Streaming, sublinear and near linear time algorithms
Keywords
  • Load Balancing
  • Semi-Streaming Algorithms
  • Semi-Matching

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