Host Configuration Optimization Using Variable CPU Weighting
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Solution Overview
Problem
Existing data center host configurations struggle to accommodate increasing compute power due to static predefined quanta of computing resources, leading to inefficiencies in workload placement and memory optimization.
Innovation Solution
Implementing a variable weighting factor based on actual workload usage profiles to dynamically optimize host configurations by calculating the CPU weighting factor and memory-to-CPU utilization ratios, allowing for dynamic workload placement on suitable compute clusters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If static predefined quanta of computing resources are used for host configuration, then configuration simplicity is maintained, but the system cannot accommodate increasing compute power and leads to workload placement inefficiencies
Solution Approach 1:
The patent applies dynamics by transitioning from static predefined quanta to dynamic host configurations that automatically adjust based on actual workload characteristics. The system continuously monitors workload behavior and reconfigures host parameters (CPU cycles, memory, storage, network) in real-time to match actual usage patterns, enabling the system to adapt to increasing compute power while maintaining operational simplicity through automated control.
Solution Approach 2:
The patent implements parameter changes by modifying host configuration parameters dynamically based on workload profiles. Instead of fixed resource allocations, the system adjusts CPU cycle quotas, memory allocation, storage capacity, and network bandwidth as variables that respond to actual workload demands. This allows the system to accommodate Moore's Law-driven compute power increases by scaling parameters proportionally to actual usage rather than relying on static predetermined values.
2Productivity
If static host configurations are used, then management overhead is reduced, but resource utilization efficiency decreases due to mismatch between allocated and actual workload requirements
Solution Approach 1:
The patent applies feedback by implementing continuous monitoring of actual workload characteristics and using this information to adjust host configurations. The system measures real-world resource consumption patterns, compares them against allocated resources, and automatically rebalances allocations to eliminate waste and improve utilization. This closed-loop control ensures resources are dynamically matched to actual demands, significantly improving productivity while the automation handles the complexity of continuous adjustment.
Solution Approach 2:
The patent implements self-service by enabling host configurations to automatically adapt to workload requirements without manual intervention. The system autonomously monitors its own resource usage, identifies optimization opportunities, and reconfigures itself to improve efficiency. This self-managing capability increases productivity through continuous optimization while the extent of automation handles the computational complexity of real-time decision-making.
3Adaptability or versatility
If predefined resource quanta are allocated, then initial setup is simplified, but the system cannot optimize memory-to-CPU ratios for different workload types
Solution Approach 1:
The patent applies local quality by tailoring host configurations to specific workload characteristics rather than applying uniform static allocations. Different workload types (compute-intensive, memory-intensive, I/O-intensive) receive customized resource profiles that match their specific requirements. This enables optimal memory-to-CPU ratios for each workload class while the system automatically determines the appropriate configuration based on workload identification, managing complexity through automated classification and profiling.
Data Source
AI summary
Systems and methods are disclosed for calculating and utilizing a variable CPU weighting factor for host configuration optimization in a data center environment. According to one illustrative embodiment, implementations may utilize actual workload profiles to generate variable CPU weighting factor(s) to optimize host configurations.