Tenant Assignment System Optimizing Cloud Hardware Utilization
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Solution Overview
Problem
In cloud computing, maintaining under-utilized hardware resources is costly and inefficient due to the challenge of optimizing tenant resource allocation across hardware resources, which can lead to suboptimal utilization and increased expenses.
Innovation Solution
A system for tenant assignment that analyzes tenant resource usage data and hardware resource data to develop a tenant assignment model, optimizing the allocation of tenants to hardware resources, consolidating them to fewer resources when possible, and accounting for constraints such as volatile tenants and specialized services, thereby improving resource utilization and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If tenants are allocated to hardware resources in a simple manner (e.g., chronological or basic assignment), then the allocation process is fast and easy to implement, but hardware resources become under-utilized and costs increase
Solution Approach 1:
The patent implements dynamic tenant assignment that adapts to changing workload conditions. The system continuously monitors resource usage patterns and reassigns tenants between hardware resources based on current utilization levels, transforming static allocation into dynamic optimization that improves resource utilization without complex manual intervention
Solution Approach 2:
The system employs feedback mechanisms by monitoring hardware resource utilization metrics and using this information to drive reassignment decisions. The feedback loop captures usage patterns, identifies under-utilized resources, and triggers automated tenant redistribution to optimize overall system utilization while maintaining operational simplicity
2Reliability
If hardware resources are maintained to meet peak demand, then service level agreements are met, but under-utilized resources incur unnecessary costs
Solution Approach 1:
The system performs preliminary analysis of tenant workload patterns and predicts future resource requirements. By anticipating demand fluctuations before they occur, the system can proactively reassign tenants to prevent both over-provisioning during low-demand periods and service level violations during peak periods, optimizing costs while maintaining reliability
Solution Approach 2:
The patent dynamically adjusts resource allocation parameters based on changing conditions. The system modifies assignment configurations in response to workload variations, transforming fixed resource provisioning into flexible parameter-driven allocation that matches actual demand and reduces waste during low-utilization periods
3Productivity
If tenant assignment is optimized for resource utilization, then costs are reduced, but the complexity of managing assignments increases
Solution Approach 1:
The system implements self-service automation where the tenant assignment mechanism manages its own optimization without external intervention. The automated system monitors resource usage, evaluates reassignment opportunities, and executes optimization decisions independently, achieving complex resource utilization optimization while keeping operational complexity low through automation
Solution Approach 2:
The patent introduces an intermediary optimization layer that sits between tenant requests and hardware resources. This intermediary component handles the complexity of assignment optimization, workload analysis, and reassignment coordination, shielding users from complexity while achieving improved resource utilization through systematic management
4Productivity
If tenants are consolidated to fewer hardware resources, then resource utilization improves, but the ability to handle volatile tenants and specialized services becomes more challenging
Solution Approach 1:
The system segments tenants into different categories based on their characteristics, such as volatile versus stable workloads and specialized service requirements. This segmentation enables differentiated assignment strategies where volatile tenants are isolated to prevent cascading effects, while specialized services receive appropriate resource guarantees, all within the consolidated architecture
Data Source
AI summary
A system for tenant assignment includes an interface and a processor. The interface is to receive a tenant resource usage data and a hardware resource data. The tenant resource usage data comprises a tenant information on a set of tenants. The hardware resource data comprises a hardware resource information on a set of hardware resources. The processor is to develop a tenant assignment model based at least in part on the tenant resource usage data and the hardware resource data, solve the tenant assignment model to determine a suggested tenant assignment comprising an assignment of the set of tenants to the set of hardware resources, and to provide the suggested tenant assignment.