AARC: Automated Affinity-aware Resource Configuration for Serverless Workflows
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913712643768320 |
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| author | Jin, Lingxiao Cai, Zinuo Chen, Zebin Zhao, Hongyu Ma, Ruhui |
| author_facet | Jin, Lingxiao Cai, Zinuo Chen, Zebin Zhao, Hongyu Ma, Ruhui |
| contents | Serverless computing is increasingly adopted for its ability to manage complex, event-driven workloads without the need for infrastructure provisioning. However, traditional resource allocation in serverless platforms couples CPU and memory, which may not be optimal for all functions. Existing decoupling approaches, while offering some flexibility, are not designed to handle the vast configuration space and complexity of serverless workflows. In this paper, we propose AARC, an innovative, automated framework that decouples CPU and memory resources to provide more flexible and efficient provisioning for serverless workloads. AARC is composed of two key components: Graph-Centric Scheduler, which identifies critical paths in workflows, and Priority Configurator, which applies priority scheduling techniques to optimize resource allocation. Our experimental evaluation demonstrates that AARC achieves substantial improvements over state-of-the-art methods, with total search time reductions of 85.8% and 89.6%, and cost savings of 49.6% and 61.7%, respectively, while maintaining SLO compliance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20846 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AARC: Automated Affinity-aware Resource Configuration for Serverless Workflows Jin, Lingxiao Cai, Zinuo Chen, Zebin Zhao, Hongyu Ma, Ruhui Distributed, Parallel, and Cluster Computing Performance Systems and Control Serverless computing is increasingly adopted for its ability to manage complex, event-driven workloads without the need for infrastructure provisioning. However, traditional resource allocation in serverless platforms couples CPU and memory, which may not be optimal for all functions. Existing decoupling approaches, while offering some flexibility, are not designed to handle the vast configuration space and complexity of serverless workflows. In this paper, we propose AARC, an innovative, automated framework that decouples CPU and memory resources to provide more flexible and efficient provisioning for serverless workloads. AARC is composed of two key components: Graph-Centric Scheduler, which identifies critical paths in workflows, and Priority Configurator, which applies priority scheduling techniques to optimize resource allocation. Our experimental evaluation demonstrates that AARC achieves substantial improvements over state-of-the-art methods, with total search time reductions of 85.8% and 89.6%, and cost savings of 49.6% and 61.7%, respectively, while maintaining SLO compliance. |
| title | AARC: Automated Affinity-aware Resource Configuration for Serverless Workflows |
| topic | Distributed, Parallel, and Cluster Computing Performance Systems and Control |
| url | https://arxiv.org/abs/2502.20846 |