Dynamic Data Center Budgeting for Changing Operating Conditions
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Solution Overview
Problem
Existing technologies face challenges in efficiently managing and updating budgets for data centers in response to changes in operating conditions, leading to potential inefficiencies and resource misallocation.
Innovation Solution
A system that monitors the operating conditions of a data center, determines changes, and dynamically updates individual device budgets within the data center to account for these changes, thereby optimizing resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If budgets for data center devices are updated dynamically in response to changing operating conditions, then resource allocation efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements dynamic budgeting by allowing device budgets to change automatically in response to real-time operating conditions such as energy consumption, workload demands, and environmental factors. The system transitions from static, fixed budgets to dynamic, adaptive budgets that adjust continuously, enabling efficient resource allocation while managing complexity through automated decision-making frameworks.
Solution Approach 2:
The system incorporates feedback mechanisms where operating condition data is continuously monitored, analyzed, and used to adjust device budgets. This closed-loop approach ensures that budget allocations respond to actual performance and conditions, improving resource efficiency while containing complexity through structured feedback protocols and automated control algorithms.
2Productivity
If real-time monitoring and dynamic budget updates are implemented, then resource efficiency is improved, but energy consumption increases
Solution Approach 1:
The system maintains continuous monitoring and budget adjustment operations to ensure optimal resource allocation at all times. By keeping the budgeting process alive and responsive without interruption, the system maximizes resource efficiency while the automated nature of the process minimizes the energy overhead compared to manual intervention methods.
Solution Approach 2:
The budgeting system performs self-adjustment based on monitored operating conditions, automatically reallocating resources without requiring constant external intervention. This self-service capability improves resource efficiency while reducing the energy consumption associated with manual monitoring and adjustment operations.
Data Source
AI summary
Techniques are disclosed for dynamically updating budgeting for utilization of resources. The system monitors the operating conditions of a network of devices that impact resource utilization by the network of devices, and the system determines actual or predicted changes in the operating conditions. For instance, the system may determine or predict changes to a topology of the network of devices, the state of individual devices in the network of devices, external limitations imposed on the network of devices, atmospheric conditions in an environment including the network of devices, changes to devices not represented in the topology of the network of devices, and other operating conditions of the network of devices. Responsive to determining an actual or predicted change in operating conditions, the system may update budgeting for the network of devices by generating updated budgets for one or more devices included in the network of devices.