Adaptation Engine for Dynamic Resource Allocation
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Solution Overview
Problem
Conventional enterprise computing systems require manual intervention to adapt to changing resource needs, often operating reactively and failing to proactively manage resource allocation based on future demands or current loads, leading to inefficiencies.
Innovation Solution
An adaptation engine within the system analyzes load information to determine future and current resource needs, automatically allocating and deallocating resources from a resource pool, such as virtual machines and memory, to ensure optimal system performance across different time periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual intervention by system operator is used to adapt to changing resource needs, then system configuration can be adjusted, but system responsiveness and efficiency deteriorate
Solution Approach 1:
The adaptation engine enables the system to automatically monitor its own resource usage, analyze load patterns, and adjust configuration without human intervention. The system serves itself by detecting resource needs and autonomously modifying virtual machine allocations, process counts, and memory settings based on real-time and historical load data.
Solution Approach 2:
The system performs preliminary analysis of load information to predict future resource needs before actual resource shortages occur. By analyzing historical patterns and current trends, the adaptation engine proactively adjusts configurations in advance, preventing resource constraints before they impact system performance.
2Reliability
If reactive resource adjustment is implemented, then system can respond to detected issues, but proactive optimization of resource allocation is lost
Solution Approach 1:
The adaptation engine analyzes historical load information and identifies patterns to predict future resource requirements. This preliminary action allows the system to proactively adjust resource allocation before actual needs arise, optimizing performance rather than merely reacting to problems after they occur.
Solution Approach 2:
The system continuously monitors actual resource usage and compares it against predicted needs, using this feedback to refine future predictions and adjustments. The feedback loop ensures that proactive adjustments are based on actual system behavior patterns, improving accuracy over time.
3Ease of manufacture
If fixed initial system configuration is used, then system setup is simple, but ability to address changing customer requirements and system loads deteriorates
Solution Approach 1:
The system transitions from a static fixed configuration to a dynamic adaptive configuration that automatically adjusts based on real-time conditions. The adaptation engine continuously modifies resource allocations, process counts, and memory settings in response to changing load patterns, maintaining optimal performance without manual reconfiguration.
Data Source
AI summary
A system may include determination of historical resource load information associated with an enterprise computing system, determination of resource needs of the enterprise computing system associated with a future time based on the historical resource load information, and, at the future time, automatic allocation and de-allocation of resources to the enterprise computing system based on the determined resource needs.