Predictive Cloud Quota Management for Target Utilization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing SaaS systems require manual intervention from customers to increase compute resource quotas, leading to inefficiencies and potential over-allocation of resources, which can result in resource waste and service disruptions.
Innovation Solution
Implementing a predictive quota management system that automatically adjusts compute resource quotas based on historical usage data and industry-specific seasonality trends, ensuring seamless and efficient allocation without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual quota increase requests are required from customers, then customers can control their resource allocation, but the process becomes burdensome and may lead to over-allocation of resources
Solution Approach 1:
The system automatically monitors customer resource usage and adjusts quotas without requiring manual customer intervention. The predictive model self-manages the quota adjustment process by detecting usage patterns and automatically increasing or decreasing quotas based on actual needs, eliminating the burden of manual ticket submissions while preventing resource waste through precise allocation.
Solution Approach 2:
The system implements continuous feedback loops by monitoring resource usage metrics and using predictive analytics to anticipate future needs. This feedback mechanism enables the system to dynamically adjust quotas in response to actual usage patterns, ensuring optimal resource allocation without manual intervention and preventing both waste and over-allocation.
2Reliability
If quotas are set to meet potential future needs, then service disruptions are prevented, but excessive surplus resources are allocated and tied up
Solution Approach 1:
The system transitions from static quota allocation to dynamic adjustment based on real-time usage monitoring and predictive analytics. Quotas are continuously adapted to match actual customer needs, allowing the system to maintain service reliability by increasing quotas only when usage patterns indicate future needs while avoiding allocation of excessive surplus resources.
Solution Approach 2:
The system changes the quota parameter dynamically based on usage metrics and predictive model outputs. By adjusting quota values in response to observed usage patterns and predicted future needs, the system ensures service continuity while optimizing resource allocation to match actual demand rather than allocating fixed excessive amounts.
3Productivity
If customers are prompted to request quota increases at 90% utilization, then resource allocation is optimized, but service disruptions may occur before the request is processed
Solution Approach 1:
The system performs preliminary actions by proactively increasing quotas before customers reach utilization thresholds that would trigger manual requests. The predictive model analyzes usage patterns and automatically adjusts quotas in advance of potential service disruptions, eliminating the gap between optimization efficiency and service availability by acting before problems occur.
4Extent of automation
If automated predictive adjustment is implemented, then manual intervention is eliminated and resource allocation is optimized, but system complexity increases
Solution Approach 1:
The system uses a universal predictive modeling framework that can be applied across multiple customers and usage scenarios. By developing a general-purpose model that handles diverse usage patterns through common mechanisms, the system achieves high automation without proportionally increasing complexity, as the same core infrastructure serves multiple functions across different customer contexts.
Data Source
AI summary
A cloud compute resource provider implements a method for automatically adjusting a quota of compute resources allocated to an individual customer subscription. The method includes determining a current usage metric for the individual customer subscription for a recent time interval; determining whether a subscription-based historical usage model has been trained on historical usage data of the individual customer subscription; and responsive to determining that the subscription-based historical usage model has been trained, executing the subscription-based historical usage model to generate a future resource usage metric predicting a usage of the customer subscription over a future time interval; and outputting a recommended adjusted resource quota for the individual subscription, the predicted future resource usage metric satisfying a target utilization of the recommended adjusted resource quota.