Dynamic Pre-Initialization Environment Provisioning
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Solution Overview
Problem
Existing technologies face challenges in accurately determining resource requirements for pre-initialization environments, leading to potential failures or wastage of resources, and struggle with scalability to accommodate future growth without incurring additional costs or downtime.
Innovation Solution
The method involves dynamically tuning pre-initialization environment provisioning and management by generating a performance-based index table based on memory efficiency, building label features, constructing a clustering model using these features, and introducing a selection policy to balance resource usage, thereby allowing for real-time scaling and resource optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-initialization environments are manually configured with static image data, then specific functionality can be provided, but resource allocation accuracy deteriorates leading to failures or wastage
Solution Approach 1:
The patent changes the parameter of resource provisioning from static image-based allocation to dynamic allocation based on runtime performance data. The system collects performance metrics during application execution and uses this data to adjust resource allocation parameters, thereby improving both reliability and measurement precision of resource requirements.
Solution Approach 2:
The system enables pre-initialization environments to self-adjust their resource allocation based on collected performance data. The environments automatically provision resources according to actual runtime needs without manual intervention, improving resource requirement determination accuracy while maintaining functionality.
2Adaptability or versatility
If pre-initialization environments are scaled to accommodate future growth, then adaptability improves, but costs increase due to over-provisioning
Solution Approach 1:
The patent implements dynamic resource allocation where pre-initialization environments adjust their resource consumption based on actual runtime performance data. The system continuously monitors performance metrics and dynamically scales resource allocation up or down, enabling adaptability without permanent over-provisioning and thus reducing overall resource consumption.
Solution Approach 2:
The system establishes a feedback loop where runtime performance data is collected, analyzed, and used to adjust resource allocation for pre-initialization environments. This feedback mechanism enables the system to adapt to growth needs while avoiding over-provisioning, as resource allocation is continuously optimized based on actual usage patterns.
3Ease of manufacture
If manual configuration methods are used, then implementation simplicity is maintained, but productivity deteriorates due to inability to adapt to changing needs
Solution Approach 1:
The patent implements self-service automation where the system automatically collects performance data, analyzes requirements, and provisions resources without manual intervention. This maintains implementation simplicity while dramatically improving productivity and adaptability to changing needs through automated decision-making and resource allocation.
Solution Approach 2:
The patent replaces manual mechanical configuration processes with automated electronic systems that use performance data and algorithms to provision resources. This substitution maintains ease of implementation while enabling rapid adaptation to changing requirements through automated, data-driven resource allocation.
4Speed
If resources are allocated based on static images, then provisioning speed is maintained, but resource efficiency worsens leading to downtime during manual adjustments
Solution Approach 1:
The patent implements continuous resource optimization where performance data collection and resource allocation adjustments occur continuously during runtime without interrupting service. This maintains provisioning speed while improving resource efficiency by eliminating downtime associated with manual adjustments and enabling seamless adaptation to changing requirements.
Data Source
AI summary
The illustrative embodiments provide for dynamic tuning of pre-initialization environment provisioning and management. An embodiment includes accepting a request from a group of applications to generate a performance-based index table for a workload based on a feature of the applications and generating the performance-based index table. The embodiment includes building a label feature by analyzing a static program feature of the applications and the performance-based index table. The embodiment includes constructing, using clustering algorithms, a model for provisioning a pre-initialization environment using the label features. The embodiment includes loading the applications into a pre-initialization environment. The embodiment includes introducing a selection policy for a switch in the pre-initialization environment in multiple applications to balance usage of a resource. The embodiment includes updating input to the model in response to monitoring a traffic of requests and collecting real time runtime data of the workload.