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Computation-Aware Data Aggregation

Authors Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, Ariel D. Procaccia



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Bernhard Haeupler
  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
D. Ellis Hershkowitz
  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
Anson Kahng
  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
Ariel D. Procaccia
  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA

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Bernhard Haeupler, D. Ellis Hershkowitz, Anson Kahng, and Ariel D. Procaccia. Computation-Aware Data Aggregation. In 11th Innovations in Theoretical Computer Science Conference (ITCS 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 151, pp. 65:1-65:38, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/LIPIcs.ITCS.2020.65

Abstract

Data aggregation is a fundamental primitive in distributed computing wherein a network computes a function of every nodes' input. However, while compute time is non-negligible in modern systems, standard models of distributed computing do not take compute time into account. Rather, most distributed models of computation only explicitly consider communication time. In this paper, we introduce a model of distributed computation that considers both computation and communication so as to give a theoretical treatment of data aggregation. We study both the structure of and how to compute the fastest data aggregation schedule in this model. As our first result, we give a polynomial-time algorithm that computes the optimal schedule when the input network is a complete graph. Moreover, since one may want to aggregate data over a pre-existing network, we also study data aggregation scheduling on arbitrary graphs. We demonstrate that this problem on arbitrary graphs is hard to approximate within a multiplicative 1.5 factor. Finally, we give an O(log n ⋅ log(OPT/t_m))-approximation algorithm for this problem on arbitrary graphs, where n is the number of nodes and OPT is the length of the optimal schedule.

Subject Classification

ACM Subject Classification
  • Theory of computation → Approximation algorithms analysis
  • Theory of computation → Scheduling algorithms
Keywords
  • Data aggregation
  • distributed algorithm scheduling
  • approximation algorithms

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