Adaptive Hardware Resource Allocation for Software Applications
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Solution Overview
Problem
It is challenging to accurately estimate and allocate hardware resources for software applications to meet desired service levels, often resulting in over-allocation and increased costs, energy consumption, and inefficient resource utilization.
Innovation Solution
An automated adaptive technique dynamically adjusts hardware resource allocations for running software applications by iteratively reducing and increasing resource allocations based on measured metrics to maintain a desired application service level, using adjustment logic, measurement logic, and service level logic to ensure optimal resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware resources are over-allocated to software applications, then application service level is maintained, but hardware costs and energy consumption increase
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring application performance metrics and automatically adjusting hardware resource allocations in real-time. The system transitions from static over-allocation to dynamic adaptive allocation, reducing resources when performance thresholds are met and increasing them when performance degradation is detected, thereby optimizing energy consumption while maintaining service levels.
Solution Approach 2:
The system employs feedback mechanisms by monitoring application performance metrics and using this information to adjust resource allocations. The feedback loop compares actual performance against target service levels and automatically modifies resource allocation decisions, enabling the system to maintain reliability while reducing unnecessary resource consumption and associated energy costs.
2Productivity
If prediction algorithms are used to allocate minimum hardware resources, then resource allocation efficiency improves, but reliability of allocation decreases due to workload complexity
Solution Approach 1:
The patent enables the system to self-adjust resource allocations by automatically monitoring its own performance metrics and making real-time allocation decisions without external intervention. This self-service approach replaces unreliable prediction algorithms with actual observed performance data, allowing the system to dynamically optimize resource allocation efficiency while maintaining accurate and reliable allocations based on real-world workload behavior.
Solution Approach 2:
The system performs preliminary resource allocation based on initial estimates, then continuously monitors performance and makes iterative adjustments. This preliminary action followed by adaptive refinement allows the system to quickly establish baseline allocations while progressively improving allocation accuracy through real-time feedback, overcoming the limitations of static prediction algorithms.
3Quantity of substance
If memory is released to reduce resource usage, then hardware resource consumption decreases, but application service level deteriorates due to increased storage I/O
Solution Approach 1:
The patent applies dynamic adjustment to memory resource allocation by continuously monitoring application performance and adapting memory allocation in real-time. Rather than static memory release decisions, the system dynamically balances memory usage against other resource constraints, adjusting allocations based on current workload conditions to prevent service level deterioration while reducing overall resource consumption.
Solution Approach 2:
The system changes operational parameters by adjusting memory allocation thresholds and monitoring multiple performance metrics simultaneously. When memory is released, the system modifies other resource parameters (such as CPU allocation or I/O priorities) to compensate and maintain service levels, demonstrating parameter changes that balance resource reduction with performance preservation.
Data Source
AI summary
Systems and methods of adjusting allocated hardware resources to support a running software application are disclosed. A system includes adjustment logic to adjust an allocation of a first hardware resource to support a running software application. Measurement logic measures at least one hardware resource metric associated with the first hardware resource. Service level logic calculates an application service level based on the measured at least one hardware resource metric. When the first application service level satisfies a threshold application service level, the allocation of the first hardware resource is iteratively reduced to reach a reduced allocation level where the application service level does not satisfy the threshold application service level. In response thereto, the allocation of the first hardware resource is increased by an increment, such that the application service level again satisfies the threshold application service level.