Sparse Exponential Histograms for Data Aggregation
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Solution Overview
Problem
As the number of clients, performance data, and monitored metrics in network-based services grows, significant resources are required to store and process the data, leading to inaccuracies and inefficiencies in system performance analysis due to data sampling.
Innovation Solution
Implementing sparse exponential histograms to efficiently aggregate performance data by omitting buckets with zero frequency counts, reducing storage and computational requirements while maintaining data accuracy through the use of a system-level aggregator that generates and combines histograms across dimensions and time intervals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data sampling is implemented to reduce the amount of monitored data stored, then storage resources are reduced, but measurement precision deteriorates due to inaccuracies in system performance data
Solution Approach 1:
The patent extracts only the essential information from performance data by using histograms to aggregate values into buckets, and further extracts only the significant buckets using sparse representation. This removes redundant data while preserving the essential performance characteristics, resolving the contradiction between reducing data quantity and maintaining measurement precision.
Solution Approach 2:
Instead of sampling individual data points to reduce storage, the patent inverts the approach by aggregating all data points into histograms first, then selectively retaining only significant buckets. This inversion allows complete data to be considered for accuracy while still achieving compression through selective retention of important aggregated information.
2Measurement precision
If the number of performance metrics and monitored data grows to improve system monitoring comprehensiveness, then measurement precision improves, but device complexity increases due to significant compute resources required for processing
Solution Approach 1:
The patent segments performance data processing into distinct stages: data collection, histogram aggregation into buckets, significance evaluation of buckets, and selective retention. This segmentation allows comprehensive monitoring of multiple metrics while reducing computational complexity at each stage through progressive data reduction and selective processing of only significant information.
Solution Approach 2:
The patent changes the parameter representation from individual data points to aggregated histogram buckets, and further changes to sparse bucket representations. This parameter transformation reduces the dimensionality and complexity of the data structure, enabling comprehensive multi-metric monitoring with reduced computational resources for processing and analysis.
3Measurement precision
If all performance data is stored to maintain data accuracy, then measurement precision is maintained, but storage resources increase significantly
Solution Approach 1:
The patent extracts essential performance characteristics by aggregating data into histogram buckets and further extracting only the significant buckets that contribute meaningfully to performance analysis. This extraction process maintains measurement precision for critical performance aspects while dramatically reducing the quantity of stored data by eliminating redundant and insignificant information.
Solution Approach 2:
Instead of storing all raw performance data points, the patent inverts the storage approach by first aggregating all data into histograms, then storing only the significant buckets. This inversion enables complete data to be processed for accuracy determination while storing only the essential aggregated information, resolving the contradiction between data accuracy and storage resources.
Data Source
AI summary
Systems and methods for efficient data aggregation with sparse exponential histograms may support a performance monitor monitoring and recording of one or more performance metrics for one of one or more dimensions. In response to receiving performance data, a performance data aggregator may parse the performance data for one of one or more performance metrics and one or more dimensions. A sparse exponential histogram generator may create a sparse exponential histogram with the parsed performance data. The sparse exponential histogram generator may transmit or store the sparse exponential histogram representation for subsequent analysis.