Automated Datacenter Configuration Verification via Cost Functions
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Solution Overview
Problem
Current datacenter reconfiguration processes are slow, error-prone, and disruptive, requiring interdisciplinary expertise and often leading to minimized changes due to complexity and risk of service disruptions, which hampers optimization and quality of service.
Innovation Solution
Automated techniques for generating, costing, and selecting optimal intermediate configurations using a computer system that applies cost functions to measure logical differences between current and target configurations, iteratively refining changes while adhering to design invariants and simulating network traffic to ensure performance validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual planning and inspection of configuration changes is performed, then correctness and reliability can be maintained, but the process becomes slow and productivity decreases
Solution Approach 1:
The system performs self-verification of configuration changes by automatically generating test cases, executing them against simulated datacenter models, and validating results without requiring manual inspection. This enables the system to self-assess the correctness of proposed changes, maintaining reliability while dramatically increasing productivity.
Solution Approach 2:
Manual mechanical inspection processes are replaced with automated computer-based verification systems that use virtual datacenter models to simulate and validate configuration changes. This substitution eliminates human labor bottlenecks while maintaining thorough verification through programmable test cases and automated execution.
2Reliability
If manual inspection and testing of configurations is performed, then correctness can be verified, but the process becomes error-prone and time-consuming
Solution Approach 1:
The system performs preliminary verification by automatically generating and executing test cases against virtual models of the datacenter before actual changes are implemented. This advance testing in a simulated environment catches errors beforehand, ensuring verification accuracy while eliminating the time-consuming manual inspection process.
Solution Approach 2:
The system creates virtual copies (models) of the datacenter configuration that can be repeatedly tested without affecting the actual system. These virtual models serve as testbeds for validating configuration changes, enabling thorough verification through multiple simulation runs without the time constraints of manual physical testing.
3Productivity
If frequent configuration changes are made to optimize datacenter performance, then utilization and quality of service improve, but service disruption and complexity increase
Solution Approach 1:
The system implements feedback loops where configuration changes are automatically tested against simulated datacenter models, and results are used to validate proposed changes before implementation. This feedback mechanism ensures that frequent changes can be made with confidence, as each change is verified for potential disruptions before being applied to the actual system.
Solution Approach 2:
The system prepares for potential service disruptions by performing advance verification in virtual models that replicate the actual datacenter environment. This beforehand cushioning identifies and mitigates potential issues before changes are implemented, allowing frequent optimizations without the harmful effects of service disruption.
4Reliability
If comprehensive testing and validation of configuration changes is performed, then correctness and reliability improve, but the process becomes complex and time-consuming
Solution Approach 1:
The verification process is segmented into discrete, manageable components: generating test cases, executing tests against virtual models, validating results, and reporting. This segmentation breaks down the complex verification process into automated modules that can be systematically executed, maintaining comprehensive validation while reducing overall complexity through automation.
Data Source
AI summary
Herein are computerized techniques for generation, costing/scoring, optimal selection, and reporting of intermediate configurations for a datacenter change plan. In an embodiment, a computer receives a current configuration of a datacenter and a target configuration. New configurations are generated based on the current configuration. A cost function is applied to calculate a cost of each new configuration based on measuring a logical difference between the new configuration and the target configuration. A particular new configuration is selected that has a least cost. When the particular configuration satisfies the target configuration, the datacenter is reconfigured based on the particular configuration. Otherwise, this process is (e.g. iteratively) repeated with the particular configuration instead used as the current configuration. In embodiments, new configurations are randomly, greedily, and/or manually generated. In an embodiment, new configurations obey design invariants that constrain which changes and/or configurations are attainable.