Multi-tenant Resource Allocation via Consumption Signatures
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Solution Overview
Problem
Multi-tenant platform operators face challenges in optimally allocating infrastructure resources such as computational capabilities and data storage, as conventional methods fail to efficiently manage resource usage and demand, leading to suboptimal performance and increased costs due to inefficient deployment of tenants across shared infrastructure.
Innovation Solution
The system constructs data signatures for users, accounts, or tenants based on their resource consumption metrics, applying machine learning and statistical analysis to identify usage patterns and trends, enabling more informed decisions on resource allocation and distribution across servers, thereby optimizing resource usage and minimizing performance issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional resource allocation methods are used in multi-tenant platforms, then implementation simplicity is maintained, but resource utilization efficiency deteriorates leading to suboptimal performance and increased costs
Solution Approach 1:
The system continuously monitors resource consumption metrics for each tenant and uses this feedback to dynamically adjust and optimize resource allocation. Machine learning models analyze usage patterns and predict future resource needs, enabling proactive reallocation decisions that improve efficiency while maintaining manageable complexity through automated control loops.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically performs resource allocation optimization without requiring manual intervention. Machine learning algorithms autonomously analyze tenant usage patterns, identify optimization opportunities, and execute reallocation decisions, allowing the system to self-optimize resource distribution while reducing operational complexity.
2Reliability
If tenants are distributed across shared infrastructure without optimization, then deployment simplicity is maintained, but platform performance deteriorates due to inefficient resource allocation
Solution Approach 1:
The system performs preliminary analysis of tenant resource usage patterns before making allocation decisions. Machine learning models pre-process historical data to identify usage trends and predict future resource requirements, enabling proactive optimization that improves platform reliability while managing complexity through advance planning and preparation.
Solution Approach 2:
The patent dynamically adjusts allocation parameters based on monitored resource consumption metrics and predicted usage patterns. The system modifies allocation decisions by changing key parameters such as resource assignment, capacity allocation, and distribution strategies, thereby improving platform performance while maintaining manageable complexity through parameter-based control.
3Productivity
If resource allocation is not optimized, then operational simplicity is maintained, but cost efficiency deteriorates due to wasted computational capabilities and storage
Solution Approach 1:
The system implements self-service optimization where machine learning algorithms automatically analyze resource consumption patterns and execute cost-efficient reallocation decisions without manual intervention. This autonomous operation improves cost efficiency by eliminating resource waste while maintaining operational simplicity through automated decision-making that requires minimal human involvement.
Data Source
AI summary
A system and associated processes to enable a multi-tenant platform operator or administrator to make more optimal decisions with regards to the allocation of platform infrastructure resources (such as computational capabilities, data storage, etc.) among one or more tenants or accounts. In some embodiments, the inventive methods construct a data “signature” for a set of identified users, accounts, or tenants, where the signature contains data regarding the user, account, or tenant's “consumption” of platform infrastructure resources.