Summary of Leveraging Interpretability in the Transformer to Automate the Proactive Scaling Of Cloud Resources, by Amadou Ba et al.
Leveraging Interpretability in the Transformer to Automate the Proactive Scaling of Cloud Resources
by Amadou Ba, Pavithra Harsha, Chitra Subramanian
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of accurately provisioning microservices in cloud-native systems to guarantee high Quality of Service (QoS) and minimize operational costs. They develop a model that captures the relationship between end-to-end latency, front-end requests, and resource utilization, leveraging the Temporal Fusion Transformer (TFT) architecture with interpretability features. The TFT predicts end-to-end latency, and when results indicate SLA non-compliance, the feature importance is used to learn adjustments required for compliance through Kernel Ridge Regression (KRR). The authors demonstrate their approach’s effectiveness in a microservice-based application and provide a roadmap for deployment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cloud services need to ensure high-quality performance. To do this, they must accurately provision each part of the service with the right amount of resources. This is hard because it depends on many factors like how busy the system is and how connected different parts are. The authors develop a model that shows how these things relate to each other. They use this model to predict how long it will take for data to travel from start to finish. If the prediction says that the service won’t meet its performance promises, they use special features of their model to figure out what needs to change to meet those promises. |
Keywords
» Artificial intelligence » Regression » Transformer
