Tenant Onboarding Prioritization via Predictive Usage Modeling
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Solution Overview
Problem
Current on-boarding processes for computing systems hosting multiple tenants are inefficient, as they require significant resources and time to configure each tenant, leading to a large footprint and high latency, without effectively prioritizing tenants based on their usage impact.
Innovation Solution
A tenant model is developed to predict likely usage patterns of tenants, allowing for the prioritization and strategic on-boarding of high-impact tenants, reducing the overall computing resources needed while achieving similar workload expansion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional on-boarding processes are used to configure each tenant, then all tenants can be onboarded to the workload, but the process requires significant computing resources and time, leading to large system footprint and high latency
Solution Approach 1:
The system performs preliminary actions by collecting tenant attributes and applying predictive models before the actual on-boarding process. This allows the system to identify and prioritize high-impact tenants in advance, so that computing resources can be allocated more efficiently when the actual on-boarding occurs, reducing both the time and resources required for the overall process.
Solution Approach 2:
The patent implements self-service through automated model-based tenant prioritization. Instead of requiring manual engineering assessment for each tenant, the system automatically collects tenant attributes, applies predictive models to estimate usage impact, and generates prioritization rankings. This automation eliminates manual intervention overhead and significantly reduces the computing resources and time required for the on-boarding process.
2Ease of operation
If engineers manually assist tenants in configuring the computing system, then desired access to the workload is achieved, but the process is time-consuming and requires significant personal resources
Solution Approach 1:
The system replaces manual engineering assistance with automated self-service capabilities. Tenant attributes are automatically collected and processed through predictive models that estimate usage impact. The system then automatically prioritizes tenants and guides the on-boarding process, eliminating the need for time-consuming manual configuration assistance while still achieving the desired outcome of proper system access.
Solution Approach 2:
The patent substitutes the mechanical system of manual engineering assistance with an automated information processing system. Instead of engineers manually assessing and configuring each tenant, the system uses automated data collection, model-based prediction, and algorithmic prioritization to replace human intervention, thereby reducing on-boarding time while maintaining operational effectiveness.
3Reliability
If all subscribed tenants are onboarded simultaneously, then complete workload coverage is achieved, but the computing system overhead and personal resources required are quite costly
Solution Approach 1:
The patent applies segmentation by dividing the tenant population into priority segments based on predicted usage impact. Instead of treating all tenants uniformly, the system segments them into high-impact and lower-impact groups using model-based predictions. This allows the system to focus resources on high-priority segments while maintaining overall workload coverage, thereby reducing system overhead while preserving reliability.
Solution Approach 2:
The system changes the parameter of tenant prioritization from uniform treatment to differentiated treatment based on predictive metrics. By introducing new parameters such as predicted usage impact and priority rankings derived from tenant attributes, the system can optimize resource allocation across different tenant segments, reducing overall system overhead while maintaining necessary workload access coverage.
Data Source
AI summary
A tenant model models workload usage of tenants, based upon a set of tenant attributes. The model is applied to a set of tenants waiting to be on-boarded to a workload to identify a metric indicative of likely tenant usage of the workload. A subset, of the set of tenants, are identified for on-boarding, based upon the metric, and on-boarding functionality is controlled to the identified subset of tenants.