Context Graphs for Datacenter State Completion
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Solution Overview
Problem
Datacenter state information is often incomplete due to differing reporting rates and frequencies, which can lead to inaccurate assessments and potential system damage, especially in large systems where anomalies may affect task outcomes.
Innovation Solution
The method involves creating a context graph representing datacenter components and their relationships, deriving a context hash to identify similar subgraphs from a library, and imputing property information to complete incomplete data using aggregation techniques based on similarity thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reporting frequency is increased to accurately assess datacenter state, then measurement precision is improved, but productivity deteriorates due to performance impact
Solution Approach 1:
The system performs preliminary actions by proactively completing incomplete state information using historical data and similarity matching before assessments are needed. Context graphs are pre-built and stored, allowing rapid completion of missing metrics without triggering high-frequency reporting, thus maintaining accuracy while avoiding performance impact.
Solution Approach 2:
The system creates copies of historical state information in the form of context graphs and subgraph libraries. Instead of querying live systems for missing data, the system copies and reuses historical context from similar subgraphs, eliminating the need for high-frequency reporting while maintaining assessment accuracy.
2Productivity
If reporting frequency is decreased to maintain performance, then productivity is improved, but measurement precision deteriorates due to incomplete state information
Solution Approach 1:
The system introduces context graphs as an intermediary layer between low-frequency reporting and state assessment. These graphs store historical relationships and metrics, allowing the system to retrieve complete state information without increasing reporting frequency. The context graphs mediate between limited data collection and comprehensive assessment needs.
Solution Approach 2:
The system creates and stores copies of historical state contexts in graph format. When assessment is needed, complete state information is copied from these pre-stored context graphs rather than querying live systems, maintaining both performance and measurement precision.
3Reliability
If more metrics are collected to ensure complete state information, then reliability is improved, but device complexity increases due to handling multiple reporting rates
Solution Approach 1:
The system segments the complex task of collecting multiple metrics with different reporting rates into separate context graphs for different subgraphs. Each subgraph captures relationships and metrics independently, allowing the system to handle incomplete data from various sources without coordinating complex reporting schedules, thus improving reliability while reducing complexity.
Solution Approach 2:
Instead of implementing complex coordination mechanisms for multiple reporting rates, the system copies historical state information into context graphs. This copying approach captures complete state information from various metrics without requiring real-time coordination of different reporting rates, simplifying the system while maintaining reliability.
Data Source
AI summary
Incomplete state information for nodes of a datacenter is completed utilizing historical state information. A context graph is received having a plurality of nodes that correspond to components of the datacenter, each node including properties that correspond to the represented component. It is determined that at least one of the properties for a node is incomplete. A context hash is derived from the context graph and compared to a plurality of subgraph hashes. Based upon the comparison, one or more subgraphs are identified that are similar to the context hash. Where a single similar subgraph is identified, the property information for the single subgraph is imputed to the context graph to complete the incomplete information. Where more than one similar subgraph is identified, the property information for the identified similar subgraphs is aggregated and the aggregated property information is imputed to the context graph to complete the incomplete information.