Customized Entity Standardization in Multi-Tenant Cloud Databases
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Solution Overview
Problem
In multi-tenant cloud systems, tenant-specific entities can consume excessive resources, leading to imbalanced loads and impacting other tenants, with existing systems lacking effective standardization to manage resource consumption.
Innovation Solution
A machine learning model is employed to predict the resource costs of customized entities, rejecting definitions that exceed thresholds and providing recommendations for modification, ensuring that customized entities adhere to resource limits, thereby optimizing resource consumption and preventing overload.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If tenant-specific entities are allowed with high customization, then adaptability is improved, but resource consumption increases and system stability deteriorates
Solution Approach 1:
The patent applies parameter changes by establishing resource consumption thresholds for customized entities. The system evaluates customization requests against predefined resource parameters (CPU, memory, storage, network I/O) and only permits customizations that remain within acceptable thresholds, thereby maintaining system stability while allowing adaptability
Solution Approach 2:
The patent implements feedback mechanisms by monitoring resource consumption of customized entities and providing alerts when thresholds are approached or exceeded. This feedback loop enables the system to dynamically adjust and reject further high-consumption customizations, preventing resource exhaustion and maintaining reliability
2Reliability
If resource consumption thresholds are enforced, then system stability is improved, but customization flexibility deteriorates
Solution Approach 1:
The patent applies partial action by allowing customizations that partially meet resource requirements while rejecting those that excessively exceed thresholds. The system permits customizations within acceptable resource bounds, providing a balanced approach that maintains stability without completely restricting customization flexibility
Solution Approach 2:
The patent implements local quality by applying different evaluation criteria to different aspects of customization. Rather than uniformly restricting all customizations, the system evaluates each customization request based on its specific resource consumption profile, allowing flexible customizations that meet local resource constraints while maintaining overall system stability
3Productivity
If automated resource evaluation is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies self-service by implementing automated self-evaluation of customization requests against resource thresholds. The system automatically evaluates, approves, or rejects customizations based on predefined criteria without requiring manual intervention, thereby improving productivity while the modular automated evaluation framework manages complexity
4Reliability
If resource monitoring and alerting are implemented, then system stability is improved, but device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms by establishing resource consumption thresholds and providing automated alerts when these thresholds are approached or exceeded. This feedback loop enables proactive resource management, allowing the system to maintain stability through automated monitoring and notification without requiring complex manual intervention systems
Data Source
AI summary
Methods, systems, and computer-readable storage media for receiving a customized entity definition (CED) from a tenant of a plurality of tenants, processing, by a machine learning (ML) model, the CED to generate a set of costs, and determining that all of the costs in the set of costs do not exceed respective threshold costs, and in response, generating a customized entity using the CED, and storing the customized entity in the database.