arXiv:2505.21331v2 Announce Type: replace-cross Abstract: In content moderation for social media platforms, the cost of delaying the review of a content is proportional to its view trajectory, which fluctuates and is apriori unknown. Motivated by such uncertain and evolving holding costs, we consider a queueing model where job states evolve based on a Markov chain with state-dependent instantaneous holding costs. We demonstrate that in the presence of such uncertain and evolving holding costs, the two canonical algorithmic principles, instantaneous-cost ($c\mu$-rule) and expected-remaining-cos
This paper addresses a fundamental challenge in AI applications like content moderation, where real-time costs are uncertain and dynamic, pushing the boundaries of existing queueing theory.
Advanced scheduling algorithms handling uncertain evolving costs are crucial for scalable and fair operation of critical digital infrastructure, directly impacting platform efficiency and user experience.
The theoretical understanding and algorithmic principles for managing queues with non-stationary, state-dependent costs are refined, paving the way for more sophisticated AI-driven resource allocation.
- · Social media platforms
- · AI developers
- · Queueing system researchers
- · Content moderation services
- · Platforms with inefficient resource allocation
- · Traditional queueing models
More efficient and cost-effective content moderation systems will emerge.
Improved real-time decision-making in other dynamic resource allocation problems, like logistics or cloud computing, could be inspired by this work.
Enhanced algorithmic fairness and reduced latency in digital services reliant on complex scheduling under uncertainty.
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