Cluster Resource Utilization Optimization via Workload-Based Metering
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Solution Overview
Problem
Traditional resource metering in multi-tenant environments is inefficient, as it is based on hardware characteristics rather than workload types, leading to suboptimal resource utilization and increased operational costs.
Innovation Solution
A system and method for optimizing cluster resource utilization by retrieving and analyzing usage information across multiple tenants, determining resource commitments, and dynamically reallocating resources based on workload types, allowing for more efficient co-location of similar workloads and metering based on data characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If resource metering is based on hardware characteristics, then resource allocation is simplified and predefined, but resource utilization efficiency deteriorates and operational costs increase
Solution Approach 1:
The patent changes the metering parameter from hardware characteristics to workload types and data characteristics. The system dynamically determines resource commitments based on actual workload behavior patterns, data access frequencies, and computational intensity, allowing resources to be allocated according to actual usage patterns rather than static hardware definitions, thereby improving utilization efficiency while maintaining allocation simplicity through automated parameter adjustment
Solution Approach 2:
The patent implements dynamic resource commitment determination that adapts to changing workload conditions. The system continuously monitors workload types and adjusts resource allocations in real-time based on actual usage patterns, transforming the static hardware-based allocation model into a dynamic workload-based model that optimizes resource utilization while maintaining operational simplicity through automated adaptation
2Ease of operation
If resources are predefined by tenant hardware characteristics, then resource allocation is straightforward, but multi-tenant resource sharing optimization is lost
Solution Approach 1:
The patent creates a universal resource commitment determination framework that works across multiple tenants and workload types. The system uses a unified approach based on workload characteristics and data patterns that can be applied universally to different tenants, enabling optimized resource sharing while maintaining straightforward operation through a single standardized process that adapts to various tenant needs
Solution Approach 2:
The patent segments resource allocation into workload-type-specific commitments rather than tenant-specific allocations. By dividing resources according to workload characteristics (e.g., batch processing, real-time analytics, interactive queries), the system enables fine-grained multi-tenant optimization while maintaining operational simplicity through automated segmentation based on workload patterns rather than manual tenant configuration
Data Source
AI summary
Systems and methods for optimizing cluster resource utilization are disclosed. Systems and methods for optimizing cluster resource utilization are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for optimizing cluster resource utilization may include: (1) retrieving cluster usage information for at least one cluster resource in a multi-tenant environment; (2) determining tenant usage for the cluster resource for each of a plurality of tenants; (3) determining a tenant resource commitment for the cluster resource for each tenant; and (4) presenting tenant usage and tenant resource commitment for each resource.