# Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

arXiv: https://arxiv.org/abs/2305.03463 · HTML: https://mlsanity.com/paper/2305.03463/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2023.

## Built on by (most cited first, 14 of 14)

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- [Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization](https://mlsanity.com/paper/2309.11994/) (2023)
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- [Surrogate-Assisted Evolutionary Reinforcement Learning Based on Autoencoder and Hyperbolic Neural Network](https://mlsanity.com/paper/2505.19423/) (2025)
- [From Observations to Parameters: Detecting Changepoint in Nonlinear Dynamics with Simulation-based Inference](https://mlsanity.com/paper/2510.17933/) (2025)
- [Online Regime-aware Calibration for Black-box Social Simulators via Posterior-assisted Evolutionary Dynamic Optimization](https://mlsanity.com/paper/2601.19481/) (2026)
- [Meta-Reinforcement Learning via Evolution for Multi-Objective Combinatorial Supply Chain Optimisation](https://mlsanity.com/paper/2606.22146/) (2026)

## Abstract (arXiv)

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services.