Data Center Asset Resource Load Balance via Dynamic Reallocation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data centers often face inefficiencies due to underutilized or overutilized asset resources, making it difficult to optimize workload processing and management, especially when balancing resources across on-premises and cloud environments.
Innovation Solution
A system and method for monitoring workloads in a data center, identifying underutilized resources, and reallocating them through a load balance configurator user interface to optimize asset allocation, enabling efficient processing and management across hybrid cloud environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If asset resources are allocated to specific workloads, then workload processing capability is improved, but resource utilization efficiency deteriorates due to underutilized assets
Solution Approach 1:
The system implements dynamic resource allocation by continuously monitoring workload performance and asset utilization metrics, then automatically reallocating assets from underutilized workloads to those needing additional capacity. This dynamic adjustment resolves the contradiction by making resource allocation flexible rather than static, allowing the system to adapt to changing workload demands and maintain both high productivity and efficient resource utilization.
Solution Approach 2:
The system employs feedback mechanisms by monitoring workload performance metrics and asset utilization levels, then using this information to drive reallocation decisions. The feedback loop identifies underutilized assets and redirects them to workloads that require additional resources, thereby simultaneously improving workload processing capability and optimizing overall resource utilization efficiency.
2Loss of energy
If resources are reallocated to balance load, then resource utilization is improved, but system complexity increases due to monitoring and management overhead
Solution Approach 1:
The system implements self-service automation where the resource allocation platform autonomously monitors workload performance, identifies underutilized assets, and executes reallocation decisions without requiring manual intervention. This automation reduces the monitoring and management overhead burden on operators while maintaining improved resource utilization, effectively resolving the contradiction between utilization gains and operational complexity.
3Device complexity
If manual resource allocation is used, then system simplicity is maintained, but workload optimization deteriorates due to inability to identify underutilized assets
Solution Approach 1:
The system performs preliminary monitoring and analysis of workload performance and asset utilization metrics to identify underutilized resources before reallocation is needed. By proactively detecting optimization opportunities and preparing reallocation strategies in advance, the system enables workload optimization while maintaining operational simplicity, as the automated identification and planning processes occur backgrounded without requiring complex manual analysis.
Data Source
AI summary
A system, method, and computer-readable medium are disclosed for performing a data center monitoring and management operation. The data center monitoring and management operation includes: identifying a plurality of asset resources within a data center; monitoring workloads within the data center, the monitoring providing data center workload data, the data center workload data including asset resource utilization data; determining whether certain asset resources are underutilized; presenting a load balance configurator user interface, the load balance configurator user interface enabling allocation of asset resources to target workloads; and, performing a data center asset allocation operation, the data center asset allocation operation reallocating workloads in response to the allocation of asset resources to target workloads.