# Intelligent Task Scheduling for Microservices via A3C-Based Reinforcement Learning

arXiv: https://arxiv.org/abs/2505.00299 · HTML: https://mlsanity.com/paper/2505.00299/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 1 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 10 of 10)

- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration](https://mlsanity.com/paper/2504.00282/) (2025)
- [Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference](https://mlsanity.com/paper/2504.05758/) (2025)
- [Contrastive and Variational Approaches in Self-Supervised Learning for Complex Data Mining](https://mlsanity.com/paper/2504.04032/) (2025)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [A Deep Learning Approach to Interface Color Quality Assessment in HCI](https://mlsanity.com/paper/2502.09914/) (2025)
- [Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction](https://mlsanity.com/paper/2504.04385/) (2025)
- [Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage](https://mlsanity.com/paper/1501.01426/) (2015)
- [A Deep Learning Framework for Boundary-Aware Semantic Segmentation](https://mlsanity.com/paper/2503.22050/) (2025)

## Built on by (most cited first, 2 of 2)

- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning](https://mlsanity.com/paper/2506.19246/) (2025)

## Abstract (arXiv)

To address the challenges of high resource dynamism and intensive task concurrency in microservice systems, this paper proposes an adaptive resource scheduling method based on the A3C reinforcement learning algorithm. The scheduling problem is modeled as a Markov Decision Process, where policy and value networks are jointly optimized to enable fine-grained resource allocation under varying load conditions. The method incorporates an asynchronous multi-threaded learning mechanism, allowing multiple agents to perform parallel sampling and synchronize updates to the global network parameters. This design improves both policy convergence efficiency and model stability. In the experimental section, a real-world dataset is used to construct a scheduling scenario. The proposed method is compared with several typical approaches across multiple evaluation metrics, including task delay, scheduling success rate, resource utilization, and convergence speed. The results show that the proposed method delivers high scheduling performance and system stability in multi-task concurrent environments. It effectively alleviates the resource allocation bottlenecks faced by traditional methods under heavy load, demonstrating its practical value for intelligent scheduling in microservice systems.