mlsanityData to 14 Oct 2026

Dynamic Scheduling Strategies for Resource Optimization in Computing Environments

arXiv 2412.17301 ↗First posted 23 Dec 2024cs.DC cs.AIcited by 6

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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.

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

18 of 29 references.

References · 18

  1. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  2. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  3. 2024Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Trainingcited by 13
  4. 2024Breast Cancer Image Classification Method Based on Deep Transfer Learningcited by 12
  5. 2024Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classificationcited by 11
  6. 2024Comprehensive Evaluation of Multimodal AI Models in Medical Imaging Diagnosis: From Data Augmentation to Preference-Based Comparisoncited by 10
  7. 2024Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphscited by 9
  8. 2024Optimizing Gesture Recognition for Seamless UI Interaction Using Convolutional Neural Networkscited by 9
  9. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  10. 2024An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reductioncited by 9
  11. 2024Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systemscited by 9
  12. 2024Robust Graph Neural Networks for Stability Analysis in Dynamic Networkscited by 8
6 more
  1. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  2. 2024Accurate Medical Named Entity Recognition Through Specialized NLP Modelscited by 7
  3. 2024Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Modelscited by 6
  4. 2024Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothingcited by 5
  5. 2024Optimizing Multi-Task Learning for Enhanced Performance in Large Language Modelscited by 5
  6. 2024Stock Type Prediction Model Based on Hierarchical Graph Neural Networkcited by 5

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Citing papers per year: 2025: 6.

6 papers.

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  5. 2025Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific Optimization of Large Language Modelscited by 4
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