Author(s)

RuiWen Zhang

Affiliation(s)

Department of Computer Science, George Washington University, Washington 20052, USA.

Corresponding Author

RuiWen Zhang

ABSTRACT

Serverless computing has emerged as a transformative paradigm in cloud infrastructure, offering dynamic resource provisioning and pay-per-use economics that significantly reduce operational overhead for application developers. However, the inherent challenges of serverless architectures, including unpredictable workload patterns, heterogeneous resource demands, and stringent quality-of-service requirements, necessitate intelligent resource allocation mechanisms that can adapt to rapidly changing conditions. This paper proposes a novel learning-based approach that leverages Graph Neural Networks (GNNs) to model the complex dependencies and resource relationships in serverless computing environments. Our framework captures the intricate structural patterns of function invocations, resource utilization, and inter-function dependencies through graph representations, enabling more effective resource allocation decisions. The GNN-based model employs a deep reinforcement learning architecture where an intelligent agent learns optimal policies through continuous interaction with the serverless environment. Through comprehensive experimental evaluation, we demonstrate that our approach achieves superior performance compared to traditional heuristic-based methods including Shortest Job First (SJF), Tetris, and Packer algorithms, reducing average job slowdown by approximately 40% under high load conditions while maintaining robust performance across varying workload intensities. The proposed system exhibits strong scalability with efficient training on graphs containing up to 10 million edges and demonstrates excellent generalization capabilities across diverse workload patterns.

KEYWORDS

 Serverless computing; Graph neural networks; Resource allocation; Deep reinforcement learning; Function as a service; Dynamic scheduling

CITE THIS PAPER

RuiWen Zhang. Learning-based dynamic resource allocation for serverless computing with graph neural networks. Journal of Trends in Applied Science and Advanced Technologies. 2025, 2(1): 70-81. DOI: https://doi.org/10.61784/asat3019.

REFERENCES

[1] Jonas E, Schleier-Smith J, Sreekanti V, et al. Cloud programming simplified: A Berkeley view on serverless computing. arXiv preprint arXiv:1902.03383, 2019.

[2] Wang Y, Qiu S, Chen Z. Neural network approaches to temporal pattern recognition: Applications in demand forecasting and predictive analytics. Journal of Banking and Financial Dynamics, 2025, 9(11): 19-32.

[3] Castro P, Ishakian V, Muthusamy V, et al. The server is dead, long live the server: Rise of serverless computing, overview of current state and future trends in research and industry. arXiv preprint arXiv:1906.02888, 2019.

[4] Liu J, Wang J, Lin H. Coordinated physics-informed multi-agent reinforcement learning for risk-aware supply chain optimization. IEEE Access, 2025, 13: 190980-190993.

[5] Ustiugov D, Petrov P, Kogias M, et al. Benchmarking, analysis, and optimization of serverless function snapshots. Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2021: 340-354.

[6] Zang H C, Wang Y J, Liu Y P, et al. Effects of water and nitrogen limited supply on stereotypic characteristics of high-yielding wheat. Pakistan Journal of Botany, 56(4).

[7] Hu H, Liu F, Pei Q, et al. λgrapher: A resource-efficient serverless system for GNN serving through graph sharing. Proceedings of the ACM Web Conference 2024, 2024: 2826-2835.

[8] Tari M, Ghobaei-Arani M, Pouramini J. Auto-scaling mechanisms in serverless computing: A comprehensive review. Computer Science Review, 2024, 53: 100650.

[9] Psychas K, Ghaderi J. Scheduling jobs with random resource requirements in computing clusters. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications, 2019: 2269-2277.

[10] Zhou G, Tian W, Buyya R, et al. Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions. Artificial Intelligence Review, 2024, 57(5): 124.

[11] Yang S, Ding G, Chen Z, et al. GART: Graph neural network-based adaptive and robust task scheduler for heterogeneous distributed computing. IEEE Access, 2025.

[12] Sreekanti V, Wu C, Chhatrapati S, et al. A fault-tolerance shim for serverless computing. Proceedings of the Fifteenth European Conference on Computer Systems, 2020: 1-15.

[13] Aslani A, Ghobaei-Arani M. Machine learning inference serving models in serverless computing: A survey. Computing, 2025, 107(1): 47.

[14] Batool I, Kanwal S. Serverless edge computing: A taxonomy, systematic literature review, current trends and research challenges. arXiv preprint arXiv:2502.15775, 2025.

[15] Tian H, Huang T, Liu M, et al. Enabling sub-second QoS-aware scheduling for dynamic serverless workloads. China Conference on Wireless Sensor Networks Singapore: Springer Nature, 2024: 104-117.

[16] Dhakal A, Kulkarni S G, Ramakrishnan K. Gslice: Controlled spatial sharing of GPUs for a scalable inference platform. Proceedings of the 11th ACM Symposium on Cloud Computing, 2020: 492-506.

[17] Lekkala C. AI-driven dynamic resource allocation in cloud computing: Predictive models and real-time optimization. J Artif Intell Mach Learn & Data Science, 2024, 2.

[18] Li H, Wang G, Li L, et al. Dynamic resource allocation and energy optimization in cloud data centers using deep reinforcement learning. Journal of Artificial Intelligence General Science, 2024, 1(1): 230-258.

[19] Tran-Dang H, Bhardwaj S, Rahim T, et al. Reinforcement learning based resource management for fog computing environment: Literature review, challenges, and open issues. Journal of Communications and Networks, 2022, 24(1): 83-98.

[20] Ullah I, Mahmood T, Ali H, et al. Molecular investigation of bacterial blight of rice in the foothills of the western Himalayas, Pakistan. Pakistan Journal of Botany, 2024, 56(4).

[21] Xiong X, Zheng K, Lei L, et al. Resource allocation based on deep reinforcement learning in IoT edge computing. IEEE Journal on Selected Areas in Communications, 2020, 38(6): 1133-1146.

[22] Khemani B, Patil S, Kotecha K, et al. A review of graph neural networks: Concepts, architectures, techniques, challenges, datasets, applications, and future directions. Journal of Big Data, 2024, 11(1): 18.

[23] Zhao X, Yang Y, Yang J, et al. Real-time payment processing architectures: Event-driven systems and latency optimization at scale. Journal of Banking and Financial Dynamics, 2025, 9(12): 10-21.

[24] Hu X, Zhao X, Wang J, et al. Information-theoretic multi-scale geometric pre-training for enhanced molecular property prediction. PLoS One, 2025, 20(10): e0332640.

[25] Mai N T, Cao W, Fang Q. A study on how LLMs (eg GPT-4, chatbots) are being integrated to support tutoring, essay feedback and content generation. Journal of Computing and Electronic Information Management, 2025, 18(3): 43-52.

[26] Wang Y, Ding G, Zeng Z, et al. Causal-aware multimodal transformer for supply chain demand forecasting: Integrating text, time series, and satellite imagery. IEEE Access, 2025.

[27] Han X, Yang Y, Chen J, et al. Symmetry-aware credit risk modeling: A deep learning framework exploiting financial data balance and invariance. Symmetry, 2025, 17(3).

[28] Sun T, Yang J, Li J, et al. Enhancing auto insurance risk evaluation with transformer and SHAP. IEEE Access, 2024.

[29] Huang S, Zhang L, Yan M, et al. Growth characteristics of aroma-enhancing bacteria in reconstituted tobacco extracts using isothermal microcalorimetry. Pakistan Journal of Botany, 2024, 56(4).

[30] Yang J, Zeng Z, Shen Z. Neural-symbolic dual-indexing architectures for scalable retrieval-augmented generation. IEEE Access, 2025.

[31] Lin H, Liu W. Symmetry-aware causal-inference-driven web performance modeling: A structure-aware framework for predictive analysis and actionable optimization. Symmetry, 2025, 17(12): 2058.

[32] Mai N T, Fang Q, Cao W. Measuring student trust and over-reliance on AI tutors: Implications for STEM learning outcomes. International Journal of Social Sciences and English Literature, 2025, 9(12): 11-17.