Computer Science > Distributed, Parallel, and Cluster Computing
arXiv:2404.03079 (cs)
[Submitted on 3 Apr 2024]
Title:vPALs: Towards Verified Performance-aware Learning System For Resource Management
View a PDF of the paper titled vPALs: Towards Verified Performance-aware Learning System For Resource Management, by Guoliang He and 3 other authors
View PDFHTML (experimental)Abstract:Accurately predicting task performance at runtime in a cluster is advantageous for a resource management system to determine whether a task should be migrated due to performance degradation caused by interference. This is beneficial for both cluster operators and service owners. However, deploying performance prediction systems with learning methods requires sophisticated safeguard mechanisms due to the inherent stochastic and black-box natures of these models, such as Deep Neural Networks (DNNs). Vanilla Neural Networks (NNs) can be vulnerable to out-of-distribution data samples that can lead to sub-optimal decisions. To take a step towards a safe learning system in performance prediction, We propose vPALs that leverage well-correlated system metrics, and verification to produce safe performance prediction at runtime, providing an extra layer of safety to integrate learning techniques to cluster resource management systems. Our experiments show that vPALs can outperform vanilla NNs across our benchmark workload.
| Comments: | presented at Deployable AI Workshop at AAAI-2024 |
| Subjects: | Distributed, Parallel, and Cluster Computing (cs.DC) |
| Cite as: | arXiv:2404.03079 [cs.DC] |
| (orarXiv:2404.03079v1 [cs.DC] for this version) | |
| https://doi.org/10.48550/arXiv.2404.03079 arXiv-issued DOI via DataCite |
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled vPALs: Towards Verified Performance-aware Learning System For Resource Management, by Guoliang He and 3 other authors
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.