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Diagnosing performance problems in distributed applications is extremely challenging. A significant reason is that it is hard to know where to place instrumentation a priori to help diagnose problems that may occur in the future. We present the vision of an automated instrumentation framework, Pythia, that runs alongside deployed distributed applications. In response to a newly-observed performance problem, Pythia searches the space of possible instrumentation choices to enable the instrumentation needed to help diagnose it. Our vision for Pythia builds on workflow-centric tracing, which records the order and timing of how requests are processed within and among a distributed application's nodes (i.e., records their workflows). It uses the key insight that localizing the sources high performance variation within the workflows of requests that are expected to perform similarly gives insight into where additional instrumentation is needed.
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Pythia: An Edge First Agent for State Prediction in High-Dimensional Environments
Modern deep learning agents usually operate in low-dimensional environments. They process pixel input, don’t offer insights into their thought process, and require significant power and computational resources. These characteristics make them inapplicable for embedded devices. In this letter, we present Pythia, an edge-first framework that uses latent imagination to handle complex environments efficiently and envision future agent states. It utilizes a VQ-VAE to reduce the high-dimensional features into a low-dimensional space, making it ideal for modern embedded devices. Moreover, Pythia offers human interpretable feedback and scales well with respect to the design space. Pythia surpassed the other state-of-art models in prediction accuracy on both intrinsic and extrinsic metrics.
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- Award ID(s):
- 2324854
- PAR ID:
- 10536432
- Publisher / Repository:
- IEEE Embedded Systems Letters
- Date Published:
- Journal Name:
- IEEE Embedded Systems Letters
- ISSN:
- 1943-0663
- Page Range / eLocation ID:
- 1 to 1
- Subject(s) / Keyword(s):
- Latent Imagination, Edge Inference, Explainability, Embedded Deep Learning, Self Attention
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LES.2024.3403090
