# Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

arXiv: https://arxiv.org/abs/2412.17301 · HTML: https://mlsanity.com/paper/2412.17301/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 29 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [Breast Cancer Image Classification Method Based on Deep Transfer Learning](https://mlsanity.com/paper/2404.09226/) (2024)
- [Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification](https://mlsanity.com/paper/2411.12151/) (2024)
- [Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparison](https://mlsanity.com/paper/2412.05536/) (2024)
- [Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphs](https://mlsanity.com/paper/2411.15195/) (2024)
- [Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networks](https://mlsanity.com/paper/2411.15598/) (2024)
- [Enhancing Few-Shot Learning with Integrated Data and GAN Model Approaches](https://mlsanity.com/paper/2411.16567/) (2024)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems](https://mlsanity.com/paper/2412.07027/) (2024)
- [Robust Graph Neural Networks for Stability Analysis in Dynamic Networks](https://mlsanity.com/paper/2411.11848/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models](https://mlsanity.com/paper/2412.03606/) (2024)
- [Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing](https://mlsanity.com/paper/2412.03097/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)
- [Stock Type Prediction Model Based on Hierarchical Graph Neural Network](https://mlsanity.com/paper/2412.06862/) (2024)

## Built on by (most cited first, 6 of 6)

- [Optimized Unet with Attention Mechanism for Multi-Scale Semantic Segmentation](https://mlsanity.com/paper/2502.03813/) (2025)
- [Joint Graph Convolution and Sequential Modeling for Scalable Network Traffic Estimation](https://mlsanity.com/paper/2505.07674/) (2025)
- [Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer](https://mlsanity.com/paper/2501.13467/) (2025)
- [Multi-Scale Transformer Architecture for Accurate Medical Image Classification](https://mlsanity.com/paper/2502.06243/) (2025)
- [Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Models](https://mlsanity.com/paper/2501.14859/) (2025)
- [A Hybrid CNN-Transformer Model for Heart Disease Prediction Using Life History Data](https://mlsanity.com/paper/2503.02124/) (2025)

## Abstract (arXiv)

The rapid development of cloud-native architecture has promoted the widespread application of container technology, but the optimization problems in container scheduling and resource management still face many challenges. This paper proposes a container scheduling method based on multi-objective optimization, which aims to balance key performance indicators such as resource utilization, load balancing and task completion efficiency. By introducing optimization models and heuristic algorithms, the scheduling strategy is comprehensively improved, and experimental verification is carried out using the real Google Cluster Data dataset. The experimental results show that compared with traditional static rule algorithms and heuristic algorithms, the optimized scheduling scheme shows significant advantages in resource utilization, load balancing and burst task completion efficiency. This shows that the proposed method can effectively improve resource management efficiency and ensure service quality and system stability in complex dynamic cloud environments. At the same time, this paper also explores the future development direction of scheduling algorithms in multi-tenant environments, heterogeneous cloud computing, and cross-edge and cloud collaborative computing scenarios, and proposes research prospects for energy consumption optimization, adaptive scheduling and fairness. The research results not only provide a theoretical basis and practical reference for container scheduling under cloud-native architecture, but also lay a foundation for further realizing intelligent and efficient resource management.