VM Capacity Planning via Time Series Forecasting
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Solution Overview
Problem
Traditional virtual machine performance monitoring relies on static thresholds, triggering alerts prematurely and only considering historical data, which can lead to unnecessary administrative actions and inefficiencies.
Innovation Solution
Implementing statistical modeling techniques to build multivariate time series and forecasting models using VM metrics, allowing for dynamic alert threshold determination and proactive capacity planning based on predicted future conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static threshold monitoring is used to detect VM performance issues, then alert thresholds can be set to trigger early warnings, but alerts are triggered prematurely and false alarms increase
Solution Approach 1:
The patent transitions from static threshold monitoring to dynamic threshold adjustment by using historical performance data and statistical models. The system continuously learns from past VM behavior patterns and adapts thresholds accordingly, allowing thresholds to evolve with changing workloads and conditions rather than remaining fixed, thereby reducing false alarms while maintaining early detection capability
Solution Approach 2:
The system performs preliminary analysis by building forecasting models that predict future VM performance based on historical data. Instead of waiting for metrics to cross static thresholds, the system proactively identifies trends and predicts potential failures before they occur, enabling administrators to take preventive action earlier and more accurately
2Ease of operation
If only historical values are monitored with static thresholds, then administrators have time to respond to alerts, but alert thresholds trigger well before actual failure occurs causing unnecessary actions
Solution Approach 1:
The system implements feedback loops where alert responses and outcomes are fed back into the statistical models. When administrators respond to alerts, this information is used to refine future threshold predictions and reduce false alarms. The system learns from past alert patterns and adjusts its behavior accordingly, improving both administrative efficiency and response accuracy over time
Solution Approach 2:
The patent changes the parameters used for threshold determination from fixed static values to dynamic values derived from statistical analysis of historical data. By transforming thresholds from constant parameters to adaptive parameters that change based on learned patterns, the system reduces unnecessary alerts while maintaining adequate warning time for administrators
3Adaptability or versatility
If static threshold monitoring is implemented, then the system is simple to operate, but it cannot adapt to changing VM performance patterns
Solution Approach 1:
The monitoring system performs self-service by automatically learning from historical data and adjusting its own thresholds without requiring manual configuration. The statistical models continuously train on incoming performance data and autonomously adapt to changing VM patterns, reducing the need for administrator intervention while improving adaptability
Data Source
AI summary
Virtual machine capacity planning techniques are disclosed. In various embodiments, a set of time series data is constructed based at least in part on virtual machine related metric values observed with respect to a virtual machine during a training period. The constructed time series data is used to build a forecast model for the virtual machine. The forecast model is used to forecast future values for one or more of the virtual machine related metrics. The forecasted future values are used to determine whether an alert condition is predicted to be met.