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Title: All-Norm Load Balancing in Graph Streams via the Multiplicative Weights Update Method
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.  more » « less
Award ID(s):
1942010
PAR ID:
10555149
Author(s) / Creator(s):
; ;
Editor(s):
Tauman_Kalai, Yael
Publisher / Repository:
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Date Published:
Volume:
251
ISSN:
1868-8969
ISBN:
978-3-95977-263-1
Page Range / eLocation ID:
251-251
Subject(s) / Keyword(s):
Load Balancing Semi-Streaming Algorithms Semi-Matching Theory of computation → Streaming, sublinear and near linear time algorithms
Format(s):
Medium: X Size: 24 pages; 866713 bytes Other: application/pdf
Size(s):
24 pages 866713 bytes
Right(s):
Creative Commons Attribution 4.0 International license; info:eu-repo/semantics/openAccess
Sponsoring Org:
National Science Foundation
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