Abstract
Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First Serve (FCFS) scheduling algorithm.
Recommended Citation
P. A. Birajdar et al., "Adaptive Workload Management for Enhanced Function Performance in Serverless Computing," Icdcn 2025 Proceedings of the 26th International Conference on Distributed Computing and Networking, pp. 276 - 277, Association for Computing Machinery, Jan 2025.
The definitive version is available at https://doi.org/10.1145/3700838.3703657
Department(s)
Computer Science
Publication Status
Open Access
Keywords and Phrases
Auto-scaling; Load Balancing; Scheduling; Serverless Computing
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Association for Computing Machinery, All rights reserved.
Publication Date
04 Jan 2025
