# Personalized Execution Time Optimization for Billion-Scale Scheduled Jobs

arXiv: https://arxiv.org/abs/2203.06158 · HTML: https://mlsanity.com/paper/2203.06158/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 11 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [Seq2Slate: Re-ranking and Slate Optimization with RNNs](https://mlsanity.com/paper/1810.02019/) (2018)
- [Offline Reinforcement Learning for Mobile Notifications](https://mlsanity.com/paper/2202.03867/) (2022)
- [Should I send this notification? Optimizing push notifications decision making by modeling the future](https://mlsanity.com/paper/2202.08812/) (2022)
- [Reinforcement Learning-based Product Delivery Frequency Control](https://mlsanity.com/paper/2012.10858/) (2020)

## Abstract (arXiv)

Scheduled batch jobs are widely used on asynchronous computing platforms to execute enterprise applications such as promotional notifications and candidate pre-computation for recommender systems. Delivering or updating information at the right time is important for user experience and execution impact, yet providing a versatile, personalized execution time optimization solution across diverse product scenarios while maintaining reasonable infrastructure costs remains challenging. In this paper, we present a deployed system that serves billions of users daily, combining learning-to-rank with a "best time policy" for execution time selection. We describe the four-stage evolution of our approach: from heuristic peak-hour rules, to pointwise ML-based activity pattern predictions, to a linear signal assembler with globally fixed weights, and finally to a contextual ensemble learner that produces per-user adaptive fusion weights via a neural policy network trained with listwise learning-to-rank objectives. We further report the discovery of cross-use-case cannibalization effects and introduce a coordination system to mitigate the problem. Our production experiments demonstrate measurable improvements in both execution efficiency and downstream product impact. We share deployment lessons including failure analyses and design decisions accumulated over four years of operating this system at scale. To our knowledge, this represents the first ML-based multi-tenant execution time optimization system deployed across different product domains at industrial scale.