Cloud Tenant Onboarding Scaling Plan via Historical Workload Prediction
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Solution Overview
Problem
Existing cloud management platforms face challenges in predicting and managing computer resource scaling for external systems during tenant onboarding and offboarding, leading to inadequate handling of bursting workloads and poor user experiences due to inflexible and inconsistent scaling approaches.
Innovation Solution
A method that generates a scaling plan by analyzing historical data and onboarding/offboarding plans to predict workload changes, determining the need for scaling, and triggering scaling actions at specific times to ensure optimal resource allocation and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If monitor-based scaling approaches are used for external systems, then resource allocation can be adjusted, but service downtime occurs and user experience deteriorates due to failure to handle bursting workload timely
Solution Approach 1:
The system performs preliminary scaling actions by predicting future workload requirements based on historical data and scheduled tenant onboarding/offboarding events. Scaling plans are generated in advance and executed proactively before workload bursts occur, ensuring service continuity while maintaining productivity during transitions.
2Adaptability or versatility
If cloud management platforms interface with multiple external dependent systems, then multi-tenant onboarding and offboarding can be supported, but scaling becomes inflexible, inconsistent, and non-repeatable
Solution Approach 1:
The system changes operational parameters by transitioning from reactive monitor-based scaling to proactive prediction-based scaling. It uses historical workload data, tenant behavior patterns, and scheduled event information to dynamically adjust scaling parameters consistently across all external systems, making scaling operations flexible yet repeatable through standardized prediction and execution workflows.
3Reliability
If advance awareness of workload is implemented for external systems, then scaling can be planned proactively, but system complexity increases due to historical data analysis and prediction requirements
Solution Approach 1:
The system creates simplified models (copies) of complex external system behaviors by analyzing historical data patterns. Instead of directly managing the complexity of multiple external systems, it generates prediction models that replicate workload patterns, allowing proactive scaling decisions to be made based on these simplified representations rather than direct system complexity.
Data Source
AI summary
An approach is provided for generating a scaling plan. Plans for onboarding first tenant(s) a cloud computing environment and offboarding second tenant(s) of the cloud computing environment are received. Historical data about behavior of tenants of the cloud computing environment is received. Based on the received plans and the historical data, a scaling plan for scaling computer resources of external systems during the onboarding and the offboarding is generated. The scaling plan specifies a timeline indicating dates and times at which changes in workloads associated with the external systems are required for the onboarding and the offboarding. Based on the scaling plan, a scaling is determined to be needed for computer resource(s) of one of the external systems. Responsive to determining that the scaling is needed, the scaling for the computer resource(s) is triggered at a date and a time indicated by the timeline.