Automated Capacity Provisioning Using Historical Performance Data
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Solution Overview
Problem
Existing capacity planning tools do not provide direct input or feedback to provisioning tools, leading to reactive and inefficient provisioning of computer systems based on static policies and historical resource usage patterns, lacking proactive management of computing needs.
Innovation Solution
An automated system that collects performance data, normalizes utilization values, and generates provisioning policies based on service level objectives (SLOs) to dynamically manage and provision computer systems, ensuring proactive and reactive responses to current and historical information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated provisioning is implemented using historical performance data and SLOs, then productivity and responsiveness are improved, but device complexity increases
Solution Approach 1:
The provisioning system automatically provisions computing resources by analyzing its own historical performance data and comparing it against SLOs, eliminating the need for manual capacity planning and reducing dependency on external tools. The system self-adjusts resource allocation based on detected patterns and performance gaps.
Solution Approach 2:
The system continuously monitors performance data, compares actual performance against SLOs, and uses this feedback loop to automatically adjust provisioning decisions. Historical performance data serves as feedback to predict future resource needs and prevent SLO violations before they occur.
2Adaptability or versatility
If static provisioning policies are used, then device complexity is reduced, but adaptability to changing resource demands deteriorates
Solution Approach 1:
The provisioning policies transition from static, pre-defined rules to dynamic, data-driven decisions. The system continuously adapts provisioning strategies based on real-time performance monitoring and historical pattern recognition, allowing policies to evolve with changing resource demands without manual intervention.
Solution Approach 2:
The system changes key provisioning parameters such as resource allocation thresholds, scaling factors, and timing decisions based on analyzed performance data. These parameters are dynamically adjusted according to detected usage patterns and current system state rather than remaining fixed.
3Ease of operation
If manual translation of performance information is required, then ease of operation is reduced, but measurement precision is maintained
Solution Approach 1:
The system introduces an automated intermediary layer that directly interfaces with both capacity planning tools and provisioning tools. This intermediary automatically translates and transforms performance data into actionable provisioning parameters, eliminating manual translation steps and preventing information loss through automated data transformation.
Solution Approach 2:
The manual mechanical process of translating performance reports into provisioning recommendations is replaced with an automated computational system. The system uses algorithms to automatically interpret performance data, identify patterns, and generate provisioning decisions, substituting human cognitive processes with automated analysis.
4Loss of time
If reactive provisioning based on current state is used, then response time is improved, but loss of time for proactive planning increases
Solution Approach 1:
The system performs preliminary provisioning actions by analyzing historical performance data to predict future resource needs before SLO violations occur. It proactively allocates resources in advance based on detected patterns, preventing performance degradation rather than merely reacting to it.
Solution Approach 2:
The system maintains continuous monitoring and analysis of performance data, enabling uninterrupted proactive planning while simultaneously executing responsive provisioning actions. This continuous operation eliminates gaps between planning and execution, maintaining both proactive and reactive capabilities concurrently.
Data Source
AI summary
The method may include collecting performance data relating to processing nodes of a computer system which provide services via one or more applications, analyzing the performance data to generate an operational profile characterizing resource usage of the processing nodes, receiving a set of attributes characterizing expected performance goals in which the services are expected to be provided, and generating at least one provisioning policy based on an analysis of the operational profile in conjunction with the set of attributes. The at least one provisioning policy may specify a condition for re-allocating resources associated with at least one processing node in a manner that satisfies the performance goals of the set of attributes. The method may further include re-allocating, during runtime, the resources associated with the at least one processing node when the condition of the at least one provisioning policy is determined as satisfied.