Towards SLO-Compliant and Cost-Effective Serverless Computing on Emerging GPU Architectures
Serverless platforms are supporting an increasing variety of applications (apps). Among these, apps such as Machine Learning (ML) inference serving can benefit significantly from leveraging accelerators like GPUs. Yet, major serverless providers, despite having GPU-equipped servers, do not offer GPU support for their serverless functions. While recent works have attempted to bridge this gap, they are agnostic to the capabilities of new-generation GPUs, thereby, overlooking several performance optimization opportunities.
Authors
- Mahmut Kandemir (ORCID: https://orcid.org/0000-0002-9940-9951)
- Aman Sharma (ORCID: https://orcid.org/0000-0002-5010-2641)
- Chita R. Das (ORCID: https://orcid.org/0000-0002-4746-7578)
- Vivek M. Bhasi (ORCID: https://orcid.org/0000-0002-6326-0665)
- Rishabh Jain (ORCID: https://orcid.org/0009-0003-4017-2093)
- Jashwant Raj Gunasekaran (ORCID: https://orcid.org/0000-0001-9607-0131)
- Ashutosh Pattnaik (ORCID: https://orcid.org/0000-0003-0367-5989)
Institutions
- Pennsylvania State University (US)
- Park University (US)
- American Rock Mechanics Association (US)
Publication Details
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1145/3652892.3700760
- Citations
- 4
- Primary Topic
- Cloud Computing and Resource Management
- Type
- article
- Field-Weighted Citation Impact
- 3.12
Funders
- National Science Foundation