Workload Tenure Prediction for Datacenter Capacity Planning
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Solution Overview
Problem
Current capacity planning in software defined datacenters is inaccurate due to predictive resource requirement calculations that fail to consider the life cycle of future workloads, leading to frequent adjustments in allocated capacity.
Innovation Solution
The implementation of machine learning approaches to predict workload tenures by analyzing the life cycles of current and completed workloads, allowing for more accurate capacity planning by determining the duration a workload will be utilized, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predictive resource requirement calculations are used for capacity planning, then capacity can be estimated for future workloads, but the predictions are inaccurate and misleading due to not considering workload life cycles
Solution Approach 1:
The system performs preliminary analysis of workload life cycles and historical data before making capacity planning decisions. By pre-processing workload characteristics, tenure patterns, and historical completion data, the system establishes accurate predictions in advance, resolving the contradiction between prediction accuracy and planning complexity
Solution Approach 2:
The patent replaces traditional mechanical predictive calculations with machine learning-based predictions. The ML model automatically analyzes workload tenures and life cycle patterns, substituting complex manual predictive calculations with an intelligent system that achieves higher accuracy without increasing operational complexity
2Reliability
If administrators frequently update capacity based on inaccurate predictions, then capacity allocation can be adjusted, but this leads to inefficiency and increased administrative workload
Solution Approach 1:
The system implements feedback loops where actual workload tenure data and capacity utilization metrics are continuously fed back into the machine learning model. This feedback mechanism allows the system to self-correct and improve predictions over time, achieving reliable capacity allocation without requiring frequent administrative interventions
Solution Approach 2:
The capacity planning system becomes self-service by automatically updating predictions and recommendations based on incoming workload data. The ML model autonomously adjusts capacity forecasts without administrator intervention, improving both reliability and administrative efficiency simultaneously
3Adaptability or versatility
If capacity planning considers future workloads without considering their life cycles, then planning can be performed, but it fails to account for actual workload duration leading to overestimation
Solution Approach 1:
The system performs preliminary classification of workloads by their life cycle characteristics and tenure patterns before capacity planning. By pre-identifying workload categories and their typical durations, the system maintains planning flexibility while achieving precise duration predictions through the machine learning model
Data Source
AI summary
Disclosed are various embodiments for automating the prediction of workload tenures in datacenter environments. In some embodiments, parameters are identified for a plurality of workloads of a software defined data center. A machine learning model is trained to determine a predicted tenure based on parameters of the workloads. A workload for the software defined data center is configured to include at least one workload parameter. The workload is processed using the trained machine learning model to determine the predicted tenure. An input to the machine learning model includes the at least one workload parameter.