Event-Driven Machine Learning Infrastructure: Performance Benchmarking of Cloud Serverless Functions and Cloud Container- Based Compute
Keywords:
Cloud Serverless Functions; Cloud Container-Based Compute; Serverless Computing; Event-Driven Architecture; Machine Learning Inference; Performance Benchmarking; Cold start; Execution Latency; Throughput; Cost Analysis.Abstract
This paper assessed Cloud Container-Based Compute and Cloud Serverless Functions as serverless compute platforms for running event-driven machine learning inference tasks. To mimic real-time event processing scenarios, both platforms were benchmarked under the same settings using a common ML model and a variety of input payload sizes. Measured and examined key performance indicators-including cold start delay, execution time, throughput, and cost- efficiency. The findings showed that Cloud Server less Functions had quicker execution times for smaller payloads and better scalability under high concurrency, whereas Cloud Container-Based Compute had shorter cold start latency across all resource configurations. While Cloud Container-Based Compute grew more affordable for bigger, long- running jobs, cost study showed Cloud Serverless Functions was more affordable for lightweight, short-duration operations. The results underlined the need of choosing compute platforms depending on particular workload needs since they showed important trade-offs between performance and cost. This bench marking study offers valuable insights for architects and developers designing scalable, event-driven ML systems in cloud-native environments.Downloads
References
(1)A. Rose, Performance Evaluation of Serverless Object, Ph.D. dissertation, California State University, Northridge, 2023.
B. N. Y. Arafath, A comparative study between microservices and serverless in the cloud, Master's thesis, OsloMet- storbyuniversitetet, 2022.
C. Lekkala, "Containerization vs. Serverless Architectures for Data Pipelines," Serverless Architectures for Data Pipelines, Feb. 1, 2023.
D. M. Naranjo, S. Risco, G. Molt6, and I. Blanquer, "A serverless gateway for event- driven machine learning inference in multiple clouds," Concurrency and Computation: Practice and Experience, vol. 35, no. 18, p. e6728, 2023.
G. Kambala, "Cloud-Native Architectures: A Comparative Analysis of Kubernetes and Serverless Computing," 2023.
I. Goswami, "Serverless Architecture for Machine Learning," 2023.
J. J. Paul, Distributed Serverless Architectures on AWS, Berkeley, CA, 2023.
L. Scotton, Engineering framework for scalable machine learning operations, 2021.
M. Rahman, "Serverless cloud computing: a comparative analysis of performance, cost, and developer expenences in container-level services," 2023.
[lO]M. Sisak, Cost-optimal AWS Deployment Configuration for Containerized Event-driven Systems, Ph.D. dissertation, 2021.
[ll]N. Kodakandla, "Serverless Architectures: A Comparative Study of Performance, Scalability, and Cost m Cloud-native Applications," Iconic Research and Engineering Journals, vol. 5, no. 2, pp. 136-150, 2021.
P. Grzesik, D.R. Augustyn, L. Wycislik, and D. Mrozek, "Serverless computing in omics data analysis and integration," Briefings in
Bioinformatics, vol. 23, no. 1, p. bbab349, 2022.
S. Eismann, Performance Engineering of Serverless Applications and Platforms, Ph.D. dissertation, Universitat Wiirzburg, 2023.
S. R. Gallardo, Serverless strategies and tools in the cloud computing continuum, Ph.D. dissertation, Universitat Politecnica de Valencia, 2023.
[lS)V. Naik, Machine Learning Using Serverless Computing, 2021
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