research-article

Competitive Online Convex Optimization With Switching Costs and Ramp Constraints

Authors:

Sonia FahmyAuthors Info & Claims

IEEE/ACM Transactions on Networking, Volume 29, Issue 2

Pages 876 - 889

https://doi.org/10.1109/TNET.2021.3053910

Published: 15 April 2021 Publication History

Abstract

We investigate competitive online algorithms for online convex optimization (OCO) problems with linear in-stage costs, switching costs and ramp constraints. While OCO problems have been extensively studied in the literature, there are limited results on the corresponding online solutions that can attain small competitive ratios. We first develop a powerful computational framework that can compute an optimized competitive ratio based on the class of affine policies. Our computational framework can handle a fairly general class of costs and constraints. Compared with other competitive results in the literature, a key feature of our proposed approach is that it can handle scenarios where infeasibility may arise due to hard feasibility constraints. Second, we design a robustification procedure to produce an online algorithm that can attain good performance for both average-case and worst-case inputs. We conduct a case study on Network Functions Virtualization (NFV) orchestration and scaling to demonstrate the effectiveness of our proposed methods.

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Cited By

Rutten DChristianson NMukherjee DWierman A(2023)Smoothed Online Optimization with Unreliable PredictionsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794427:1(1-36)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3579442

Index Terms

Competitive Online Convex Optimization With Switching Costs and Ramp Constraints
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation

Index terms have been assigned to the content through auto-classification.

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cover image IEEE/ACM Transactions on Networking

IEEE/ACM Transactions on Networking Volume 29, Issue 2

April 2021

475 pages

ISSN:1063-6692

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1558-2566 © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 15 April 2021

Published in TON Volume 29, Issue 2

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Cited By

Rutten DChristianson NMukherjee DWierman A(2023)Smoothed Online Optimization with Unreliable PredictionsProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794427:1(1-36)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3579442

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