We examine the problem of designing learning-augmented algorithms for metrical task systems (MTS) that exploit machine-learned advice while maintaining rigorous, worst-case guarantees on performance. We propose an algorithm, DART, that achieves this dual objective, providing cost within a multiplicative factor (1+ϵ)
of the machine-learned advice (i.e., consistency) while ensuring cost within a multiplicative factor 2O(1/ϵ)
of a baseline robust algorithm (i.e., robustness) for any ϵ>0
. We show that this exponential tradeoff between consistency and robustness is unavoidable in general, but that in important subclasses of MTS, such as when the metric space has bounded diameter and in the k
-server problem, our algorithm achieves improved, polynomial tradeoffs between consistency and robustness.
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This content will become publicly available on May 20, 2025
Learning-augmented Online Minimization of Age of Information and Transmission Costs
We consider a discrete-time system where a resource-constrained source (e.g., a small sensor) transmits its
time-sensitive data to a destination over a time-varying wireless channel. Each transmission incurs a fixed transmission cost (e.g., energy cost), and no transmission results in a staleness cost represented by the Age-of-Information. The source must balance the tradeoff between transmission and staleness costs. To address this challenge, we develop a robust online algorithm to minimize the sum of transmission and staleness costs, ensuring a worst-case performance guarantee. While online algorithms are robust, they are usually overly conservative and may have a poor average performance in typical scenarios. In contrast, by leveraging historical data and prediction models, machine learning (ML) algorithms perform well in average cases. However, they typically lack worst-case performance guarantees. To achieve the best of both worlds, we design a learning-augmented online algorithm that exhibits two desired properties: (i) consistency: closely approximating the optimal offline algorithm when the ML prediction is accurate and trusted; (ii) robustness: ensuring worst case performance guarantee even ML predictions are inaccurate. Finally, we perform extensive simulations to show that our online algorithm performs well empirically and that our learning augmented algorithm achieves both consistency and robustness.
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- Award ID(s):
- 2107363
- PAR ID:
- 10532083
- Publisher / Repository:
- IEEE
- Date Published:
- Subject(s) / Keyword(s):
- Age-of-Information transmission cost online algorithm learning-augmented algorithm
- Format(s):
- Medium: X
- Location:
- Proceedings of IEEE INFOCOM 2024, Workshop on Age and Semantics of Information (ASoI)
- Sponsoring Org:
- National Science Foundation
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