Availability-Estimate Configuration Generation for Service Providers
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Solution Overview
Problem
Current methods for generating Availability Management Framework (AMF) configurations are complex and resource-intensive, often resulting in suboptimal configurations due to the need for extensive evaluation of multiple options, which can be impractical for complex systems, and may not meet service availability requirements.
Innovation Solution
An availability-estimate based method that estimates service availability early in the configuration generation process to eliminate non-viable options, prioritizing configurations that can guarantee the requested level of availability, thereby reducing the number of configurations to be considered and avoiding resource-intensive analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple configurations are generated to satisfy availability requirements through combinatorial exploration, then the availability requirement is met, but the evaluation becomes resource extensive and may not be possible for complex configurations
Solution Approach 1:
The patent applies preliminary action by estimating service availability early in the configuration generation process, before full configuration evaluation. This early estimation allows the system to identify and eliminate non-viable configuration options upfront, avoiding resource-intensive full evaluations of configurations that cannot meet availability requirements.
Solution Approach 2:
The patent extracts and eliminates non-viable configuration options from the search space based on early availability estimation. By removing configurations that cannot satisfy availability requirements before full evaluation, the system reduces the number of configurations that need detailed analysis, thereby improving efficiency.
2Productivity
If a single configuration is generated, then the configuration generation process is simple, but the single configuration may not be optimal or meet availability requirements
Solution Approach 1:
The patent performs preliminary availability estimation on multiple configuration options early in the process, allowing the system to maintain simplicity while ensuring availability requirements are met. This early assessment enables filtering of configurations that cannot satisfy availability before detailed generation and evaluation.
Solution Approach 2:
The patent changes the parameter of configuration evaluation by introducing early availability estimation as a filtering criterion. This parameter change allows the system to evaluate configurations based on availability potential before full generation, balancing simplicity with reliability assurance.
3Manufacturing precision
If extensive evaluation of multiple configuration options is performed, then the optimal configuration can be found, but the process becomes complex and resource-intensive
Solution Approach 1:
The patent extracts and removes non-viable configuration options from further consideration based on early availability estimation. This extraction reduces the set of configurations requiring detailed evaluation, thereby reducing complexity while preserving the ability to find optimal configurations among viable options.
Solution Approach 2:
The patent performs preliminary filtering of configuration options based on availability estimation before detailed evaluation. This preliminary action reduces the complexity of the overall process by eliminating configurations that cannot meet requirements, allowing focused optimization on viable options only.
Data Source
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AI summary
A system is adapted to generate a configuration for a service provider system to provide a highly available (HA) service. The system first identifies type stacks that provide the HA service and one or more component types in each type stack. Each type stack is a combination of prototypes that describe features and capabilities of available software providing the HA service. The system estimates, for each component type in the type stacks, a mean-time- to-recover (MTTR) of the HA service based on time for completing an actual recovery action in response to a component failure. The system further estimates service availability provided by each type stack based on the MTTR and a mean-time-to-failure (MTTF) of each component type in the type stack. The system then eliminates one or more of the type stacks that do not satisfy a requested service availability before proceeding to subsequent steps of configuration generation.