SDDC Time Estimation Using Granular Historical Data
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Solution Overview
Problem
Conventional time estimation methods for operations in software-defined data centers (SDDCs) are often inaccurate, leading to increased IT costs and potential downtime due to overestimation of upgrade times, which can result in breaches of contract and loss of productivity.
Innovation Solution
The implementation of a high-quality time estimation (HQTE) methodology using VMware Analytics Cloud (VAC) data, which collects and analyzes past upgrade timelines to provide precise, real-time estimates by segmenting and querying granular time-based data sets, leveraging machine learning and collective data sharing to refine estimates based on similar operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional time estimation methods are used for SDDC operations, then the estimation process is simple, but the accuracy of time estimates is poor leading to overestimation
Solution Approach 1:
The system collects actual time data from completed SDDC operations and feeds it back to continuously refine and update the time estimation model. This feedback mechanism allows the system to learn from historical data and improve estimation accuracy over time without requiring complex manual adjustments
Solution Approach 2:
The system creates a virtual model that replicates the characteristics and behavior of actual SDDC operations based on historical data. This copied model can then be used to estimate times for future operations without requiring physical testing or complex analytical calculations
2Loss of time
If accurate time estimates are provided, then downtime and IT costs are reduced, but the data collection and analysis process becomes more complex
Solution Approach 1:
The system automatically collects time data from SDDC operations without requiring manual input or intervention. The data collection process is self-service, where the system itself gathers, stores, and processes the necessary information, eliminating the need for additional manual data entry procedures
Solution Approach 2:
The system performs preliminary data collection and analysis during and after SDDC operations, preparing estimation models in advance. This preliminary action ensures that when time estimates are needed, the system already has the necessary data processed and ready, reducing the complexity of on-demand data gathering
3Ease of operation
If conventional estimation methods are used, then the system is easier to operate, but IT costs increase due to overestimation and extended downtime
Solution Approach 1:
The time estimation system operates autonomously, automatically collecting data, processing information, and generating estimates without requiring manual intervention. This self-service capability maintains ease of operation while significantly improving accuracy to reduce costly downtime and resource allocation errors
4Productivity
If accurate time estimation is implemented, then productivity is improved, but the system requires more complex data processing capabilities
Solution Approach 1:
The system implements continuous feedback loops where actual operation times are compared with estimated times, and the differences are used to refine the estimation model. This automated feedback process improves productivity by providing increasingly accurate predictions without requiring complex manual analysis
Solution Approach 2:
The system dynamically adjusts estimation parameters based on historical data patterns, operation types, and system conditions. By automatically changing parameters based on learned patterns rather than using fixed complex models, the system improves productivity while managing computational complexity
Data Source
AI summary
Accurate time estimates for operations performed on an SDDC are disclosed. The method includes receiving information about a job performed on an SDDC from at least one of a plurality of different reporting SDDC's, the information including a description of the SDDC, a description of the job performed on the SDDC, and a plurality of time stamps, each time stamp indicative of an operation performed on the SDDC in order to complete the job. The information is stored in a database in a granular time-based data set. When a request for a time estimate for a yet-to-be-performed job is received from at least a second SDDC (the request including a description of the SDDC), the stored information is used in conjunction with the description of the second SDDC to generate a time estimate for the yet-to-be-performed job.