Comparative Analysis of Server-Based and Serverless Service Performance on Google Cloud Platform (GCP) (Case Study: Machine Learning Model Deployment)
Abstract
Cloud infrastructure providers such as GCP provide various computing services to deploy applications such as machine learning models, namely server-based and serverless. However, the two services each have different characteristics and advantages so that this becomes a difficulty factor for users in choosing cloud services. This research was conducted to compare server-based and serverless services with the aim of knowing the best service resulting from the analysis of performance measurements, namely CPU and memory utilization, latency, pricing, and developer experiences. The application of machine learning models is carried out on Compute Engine and Vertex AI services and will be tested for performance through requests to endpoints 100 times using JMeter for 30 minutes. The findings show that Vertex AI performance is better than Compute Engine with CPU utilization of 0.10%, memory utilization of 0.94%, and latency of 17.34ms but the cost efficiency is owned by the Compute Engine.
Downloads
References
N. Ramsari and A. Ginanjar, ‘Implementasi Infrastruktur Server Berbasis Cloud Computing Untuk Web Service Berbasis Teknologi Google Cloud Platform’, Conf. SENATIK STT Adisutjipto Yogyak., vol. 7, pp. 169–182, 2022, doi: 10.28989/senatik.v7i0.472.
A. Fadil, ‘Strategi Efisiensi Energi dan Penyeimbangan Beban Kerja Layanan Cloud Computing Melalui Konsolidasi Mesin Virtual Dinamis’, Appl. Technol. Comput. Sci. J., vol. 3, no. 1, Art. no. 1, 2020, doi: 10.33086/atcsj.v3i1.1680.
A. Abraham and J. Yang, ‘A Comparative Analysis of Performance and Usability on Serverless and Server-Based Google Cloud Services’, in Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), vol. 700, K. Daimi and A. Al Sadoon, Eds., in Lecture Notes in Networks and Systems, no. ACR 2023, vol. 700. , Cham: Springer Nature Switzerland, 2023, pp. 408–422. doi: 10.1007/978-3-031-33743-7_33.
A. Abraham and J. Yang, ‘Analyzing the System Features, Usability, and Performance of a Containerized Application on Serverless Cloud Computing Systems’. Research Square, 2023. doi: 10.21203/rs.3.rs-3167840/v1.
I. Lee and Y. J. Shin, ‘Machine learning for enterprises: Applications, algorithm selection, and challenges’, Bus. Horiz., vol. 63, no. 2, Art. no. 2, 2020, doi: 10.1016/j.bushor.2019.10.005.
A. Arbain, M. A. Muhammad, T. Septiana, and H. D. Septama, ‘Komparasi Implementasi Model Machine Learning Hoax News pada Local dan Cloud Computing Deployment Menggunakan Google App Engine’, J. Inform. Dan Tek. Elektro Terap., vol. 10, no. 3, Art. no. 3, 2022, doi: 10.23960/jitet.v10i3.2646.
R. Xu, ‘A Design Pattern for Deploying Machine Learning Models to Production’. Computer Science and Information Systems California State University San Marcus, 2020.
W. Zhu et al., ‘QuakeFlow: a scalable machine-learning-based earthquake monitoring workflow with cloud computing’, Geophys. J. Int., vol. 232, no. 1, Art. no. 1, 2022, doi: 10.1093/gji/ggac355.
F. Rahman, ‘Serverless Cloud Computing: A Comparative Analysis of Performance, Cost, and Developer Experiences in Container-Level Services’, Thesis, Aalto University School of Science, 2023.
I. P. T. K. Wiranata, A. A. I. I. Paramitha, and I. P. Satwika, ‘Analisis Perbandingan Performa App Engine dan Compute Engine Pada Google Cloud Platform dalam Memprediksi Penyakit Mata dengan Model CNN’, JATI J. Mhs. Tek. Inform., vol. 7, no. 6, pp. 3968–3977, 2023.
Y. Wu, T. T. A. Dinh, G. Hu, M. Zhang, Y. M. Chee, and B. C. Ooi, ‘Serverless Data Science - Are We There Yet? A Case Study of Model Serving’, in Proceedings of the 2022 International Conference on Management of Data, Philadelphia, PA, USA: ACM, 2022, pp. 1866–1875. doi: 10.1145/3514221.3517905.
H. B. Barua, ‘Data science and Machine learning in the Clouds: A Perspective for the Future’, no. arXiv:2109.01661. arXiv, 2021. doi: 10.48550/arXiv.2109.01661.
M. Eisa, M. Younas, K. Basu, and I. Awan, ‘Modelling and Simulation of QoS-Aware Service Selection in Cloud Computing’, Simul. Model. Pract. Theory, vol. 103, p. 102108, 2020, doi: 10.1016/j.simpat.2020.102108.
A. K. C, S. B, and N. R, ‘Resource Utilization Prediction in Cloud Computing using Hybrid Model’, Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 4, 2021, doi: 10.14569/IJACSA.2021.0120447.
S.-Y. Hsieh, C.-S. Liu, R. Buyya, and A. Y. Zomaya, ‘Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers’, J. Parallel Distrib. Comput., vol. 139, pp. 99–109, 2020, doi: 10.1016/j.jpdc.2019.12.014.
A. Ibrahim, A. H. Alang, Madi, Baharuddin, M. A. Ahmad, and Darmawati, Metodologi Penelitian. Jakarta: Gunadarma Ilmu, 2018.
B. Erinle, Performance Testing with JMeter 3, Third. Birmingham: Packt Publishing, 2017.
Sugiyono, Metode Penelitian Kuantitatif, Kualitatif dan R&D. Bandung: Alfabeta, 2013.
B. Arifwidodo, V. Metayasha, and S. Ikhwan, ‘Analisis Kinerja Load Balancing pada Server Web Menggunakan Algoritma Weighted Round Robin pada Proxmox VE’, J. Telekomun. Dan Komput., vol. 11, no. 3, Art. no. 3, 2021, doi: 10.22441/incomtech.v11i3.11775.
A. Abouaomar, S. Cherkaoui, Z. Mlika, and A. Kobbane, ‘Resource Provisioning in Edge Computing for Latency Sensitive Applications’. arXiv, 2022. Accessed: Jun. 22, 2024.
C. J. Theaker and G. R. Brookes, ‘Memory Management — Paging Algorithms and Performance’, in Concepts of Operating Systems, C. J. Theaker and G. R. Brookes, Eds., London: Macmillan Education UK, 1993, pp. 77–102. doi: 10.1007/978-1-349-11511-2_6.
Download PDF: 254 times
Copyright (c) 2024 Journal of Information Systems and Informatics
This work is licensed under a Creative Commons Attribution 4.0 International License.
- I certify that I have read, understand and agreed to the Journal of Information Systems and Informatics (Journal-ISI) submission guidelines, policies and submission declaration. Submission already using the provided template.
- I certify that all authors have approved the publication of this and there is no conflict of interest.
- I confirm that the manuscript is the authors' original work and the manuscript has not received prior publication and is not under consideration for publication elsewhere and has not been previously published.
- I confirm that all authors listed on the title page have contributed significantly to the work, have read the manuscript, attest to the validity and legitimacy of the data and its interpretation, and agree to its submission.
- I confirm that the paper now submitted is not copied or plagiarized version of some other published work.
- I declare that I shall not submit the paper for publication in any other Journal or Magazine till the decision is made by journal editors.
- If the paper is finally accepted by the journal for publication, I confirm that I will either publish the paper immediately or withdraw it according to withdrawal policies
- I Agree that the paper published by this journal, I transfer copyright or assign exclusive rights to the publisher (including commercial rights)