Dataset Distillation for Bootstrapping Operational State Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Newly deployed computing systems face challenges in determining their operational state due to data constraints, communication costs, privacy concerns, and storage limitations, as they lack initial data for training models, which slows down their ability to manage and orchestrate operations effectively.
Innovation Solution
Implementing a dataset distillation process that captures historical telemetry and provenance information from existing nodes, creating a compact, distilled dataset that can quickly bootstrap Machine Learning models for newly deployed systems, enabling efficient and privacy-enhanced knowledge sharing to predict operational states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a model for a newly deployed node considers experiences from multiple previously deployed models to obtain good production performance, then model accuracy is improved, but data storage requirements become cost prohibitive or impossible given node compute configuration
Solution Approach 1:
The patent extracts only the essential knowledge patterns from historical telemetry data through dataset distillation, creating a compact representation that captures the core operational states without storing the complete historical dataset. This allows the newly deployed node to learn from multiple previous nodes' experiences while maintaining minimal storage requirements.
Solution Approach 2:
The patent transforms the large-scale historical telemetry data into a distilled dataset with modified parameters - reducing data volume while preserving critical information about operational states. This parameter transformation enables the model to achieve high accuracy without requiring proportional storage capacity.
2Measurement precision
If sufficient historical data is transmitted from similar nodes to a newly deployed node for model training, then model performance is improved, but communication costs and processing delays increase significantly
Solution Approach 1:
The patent extracts only the critical knowledge components from historical data through dataset distillation, transmitting a compressed representation rather than the full historical dataset. This dramatically reduces communication bandwidth requirements while preserving the essential information needed for model training and operational state prediction.
3Measurement precision
If a node stores sufficient historical data to capture its complete historical profile for training a bootstrap model, then model training quality is improved, but storage constraints prevent any node from keeping sufficient data
Solution Approach 1:
The patent applies dataset distillation to extract the essential training signals from historical telemetry data, creating a compact distilled dataset that captures the complete historical profile in a space-efficient format. This enables nodes to maintain high-quality training data within their storage constraints.
Solution Approach 2:
The patent combines knowledge from multiple nodes' distilled datasets to create a comprehensive training set for the newly deployed node. By merging distilled representations from several sources rather than storing complete historical data from a single node, the system achieves high training quality while respecting individual node storage limitations.
4Reliability
If a newly deployed node trains a model from its own data, then data privacy is maintained, but the node cannot leverage models for management and orchestration for a long while
Solution Approach 1:
The patent enables preliminary model training by allowing newly deployed nodes to leverage distilled datasets from previously deployed nodes. This preliminary action provides immediate model capabilities for management and orchestration, while the distilled dataset format preserves data privacy through knowledge abstraction rather than raw data sharing.
Data Source
AI summary
One example method includes, at a node, installing a default parametrization configuration that facilitates performance of a domain task, obtaining, by the node, a distilled dataset, and obtaining the distilled dataset is either: obtaining the distilled dataset from another node; or leveraging a synthetic state assembled in the node to select the distilled dataset from another node based on state similarity of the node to the another node. The example method further includes training a model at the node, and the training is performed using the distilled dataset, and the trained model is operable to leverage information received by the node to propose changes to the parametrization configuration so as to optimize execution of a task by the node.