Software Component Resource Allocation via Predictive Monitoring
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Solution Overview
Problem
In dynamic computing environments like cloud platforms, managing computing resources for software applications with multiple interacting components is challenging due to variable workloads, leading to issues of over-provisioning and under-provisioning, which result in resource waste and performance degradation.
Innovation Solution
A method that monitors the present conditions of software components, estimates future resource consumption using application and component models, and dynamically allocates computing resources to each component based on these estimates, considering correlations among components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If computing resources are allocated statically according to historical workloads, then resource allocation is simple, but it cannot adapt to variable workloads causing over-provisioning and under-provisioning
Solution Approach 1:
The system performs preliminary estimation of future resource consumption by analyzing present conditions of software components and their correlations. This predictive approach allows the system to allocate resources proactively before actual demand occurs, enabling adaptation to variable workloads while maintaining manageable complexity through automated prediction models.
Solution Approach 2:
The system continuously monitors present conditions of software components and uses this feedback to refine future consumption estimates. This closed-loop feedback mechanism enables the system to adapt to changing workloads dynamically, adjusting resource allocation based on actual component behavior and correlations observed in the system.
2Adaptability or versatility
If computing resources are allocated dynamically according to contingent needs, then resource allocation adapts to variable workloads, but response time is delayed until critical conditions are detected
Solution Approach 1:
The system estimates future resource consumption based on present conditions before critical conditions actually occur. By performing this prediction in advance, the system can initiate resource allocation adjustments proactively, eliminating the delay that would otherwise occur while waiting for critical conditions to manifest.
Solution Approach 2:
The system implements dynamic resource allocation by continuously updating consumption estimates based on changing present conditions of software components. This dynamic approach allows the system to respond to workload variations in real-time, adjusting resource allocation before critical conditions occur rather than reacting after detection.
3Loss of energy
If resource allocation is optimized for each software component individually, then resource waste is reduced, but correlations among components are not considered
Solution Approach 1:
The system merges the resource allocation decisions for individual software components by considering their correlations and interactions. Instead of optimizing components in isolation, the system combines their consumption patterns and allocates resources holistically, reducing overall resource waste while managing the complexity through integrated modeling of component relationships.
Data Source
AI summary
A solution is proposed for resource management of a software application including a plurality of software components interacting with each other. A corresponding method includes monitoring present conditions of the software components and estimating a future consumption of one or more computing resources by each software component from the present conditions of the software components; an allocation of the computing resources to the software components is then controlled accordingly. A computer program and a computer program product for performing the method are also proposed. Moreover, a system for implementing the method is proposed.