Workload Model for Time Series Database Storage Optimization
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Solution Overview
Problem
Managing time series databases in multi-tenant networks is challenging due to large amounts of similar data, leading to inefficiencies in storage and throughput, particularly in cloud environments where multiple users access shared databases.
Innovation Solution
A method and apparatus that utilize a workload model to classify and group time series data workloads based on parameters like workload type, storage size, and charge amount, allowing for efficient storage and execution, with features such as vector space classification, delta value calculation, and automatic time window adjustment to optimize storage and predict future data needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time series data is stored in a multi-tenant database, then data sharing and resource utilization are improved, but storage efficiency and throughput deteriorate due to large amounts of similar data
Solution Approach 1:
The patent merges multiple tenant workloads into a unified time series database, allowing data sharing across tenants while improving resource utilization. The workload model groups similar time series data from different tenants, enabling efficient storage and processing of aggregated data rather than maintaining separate database instances for each tenant.
Solution Approach 2:
The patent changes the parameter organization by introducing workload models that classify and group time series data based on parameters such as workload type, storage size, and charge amount. This parameter-based organization transforms the storage structure from tenant-centric to workload-centric, improving storage efficiency while maintaining multi-tenant data sharing capabilities.
2Device complexity
If workload data is organized without classification, then system complexity is reduced, but storage efficiency and data management capability deteriorate
Solution Approach 1:
The patent segments workload data into distinct categories using workload models that classify data based on workload type, storage size, and charge amount. This segmentation organizes the heterogeneous time series data into manageable groups, enabling efficient storage and retrieval operations while maintaining clear data governance and management capabilities.
3Ease of operation
If storage space is allocated without prediction, then resource allocation simplicity is maintained, but storage capacity utilization deteriorates
Solution Approach 1:
The patent performs preliminary actions by using workload models to predict future storage requirements before data is actually ingested. The system classifies workloads and estimates their storage needs in advance, allowing proactive resource allocation and capacity planning. This predictive approach optimizes storage capacity utilization while maintaining simple resource management through automated forecasting.
Data Source
AI summary
A method of managing time series data workload requests includes receiving a workload job request from a user in a multi-tenant network, the request specifying a plurality of workloads, each workload including time series data configured to be stored in a time series database (TSDB), inputting workload information to a workload model that is specific to the user, and classifying each workload according to the workload model, the workload model configured to classify each workload based on a plurality of parameters, the plurality of parameters including at least a workload type and an amount of storage associated with each workload. The method also includes assigning each workload of the plurality of workloads into one or more workload groups based on the classifying, and executing each workload according to the workload type and the storage size.