research-article

Online load balancing under graph constraints

Authors:

Sharayu Moharir,

Sujay Sanghavi,

Sanjay ShakkottaiAuthors Info & Claims

IEEE/ACM Transactions on Networking (TON), Volume 24, Issue 3

Pages 1690 - 1703

https://doi.org/10.1109/TNET.2015.2442597

Published: 01 June 2016 Publication History

Abstract

In several data center settings, each arriving job may only be served by one of a subset of servers. Such a graph constraint can arise due to several reasons. One is locality of the data needed by a job; for example, in content farms (e.g., in Netflix or YouTube) a video request can only be served by a machine that possesses a copy. Motivated by this, we consider a setting where each job, on arrival, reveals a deadline and a subset of servers that can serve it. The job needs to be immediately allocated to one of these servers, and cannot be moved thereafter. Our objective is to maximize the fraction of jobs that are served before their deadlines. For this online load balancing problem, we prove an upper bound of 1---1/e on the competitive ratio of nonpreemptive online algorithms for systems with a large number of servers. We propose an algorithm - INSERT RANKING - which achieves this upper bound. The algorithm makes decisions in a correlated random way and it is inspired by the work of Karp, Vazirani, and Vazirani on online matching for bipartite graphs. We also show that two more natural algorithms, based on independent randomness, are strictly suboptimal, with a competitive ratio of 1/2.

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Cited By

Aslan FIosifidis GAyala-Romero JGarcia-Saavedra ACosta-Perez X(2024)Fair Resource Allocation in Virtualized O-RAN PlatformsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390438:1(1-34)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3639043
Weng WZhou XSrikant R(2021)Optimal Load Balancing with Locality ConstraintsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34283304:3(1-37)Online publication date: 15-Jun-2021
https://dl.acm.org/doi/10.1145/3428330
Sandhu JVerma ARana P(2020)An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-020-07028-4112:1(325-352)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s11277-020-07028-4
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Online load balancing under graph constraints
1. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
    2. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 24, Issue 3

June 2016

638 pages

ISSN:1063-6692

Editor:
R. Srikant
Univ. Illinois at Urbana-Champaign, Urbana, IL

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IEEE Press

Publication History

Published: 01 June 2016

Published in TON Volume 24, Issue 3

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Cited By

Aslan FIosifidis GAyala-Romero JGarcia-Saavedra ACosta-Perez X(2024)Fair Resource Allocation in Virtualized O-RAN PlatformsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390438:1(1-34)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3639043
Weng WZhou XSrikant R(2021)Optimal Load Balancing with Locality ConstraintsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34283304:3(1-37)Online publication date: 15-Jun-2021
https://dl.acm.org/doi/10.1145/3428330
Sandhu JVerma ARana P(2020)An Expert Approach for Data Flow Prediction: Case Study of Wireless Sensor NetworksWireless Personal Communications: An International Journal10.1007/s11277-020-07028-4112:1(325-352)Online publication date: 1-May-2020
https://dl.acm.org/doi/10.1007/s11277-020-07028-4
Deng HHou IHou IDeng H(2018)Optimal Capacity Provisioning for Online Job Allocation With Hard Allocation Ratio RequirementIEEE/ACM Transactions on Networking10.1109/TNET.2018.279439426:2(724-736)Online publication date: 1-Apr-2018
https://dl.acm.org/doi/10.1109/TNET.2018.2794394

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