Dynamic Resource Request Right-Sizing for Scalable Applications
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Solution Overview
Problem
In dynamically scalable computing environments, such as cloud computing, users face challenges in accurately estimating resource needs, leading to overestimation or underestimation, resulting in inefficiency, misallocation, latency, and unnecessary costs, whether in third-party or internal systems.
Innovation Solution
A method for right-sizing resource requests involves monitoring resource usage and requests, determining a usage upper bound, testing against thresholds, and applying adjustments dynamically, with optional user confirmation, and using machine learning to modify thresholds and bounds based on performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users estimate resource needs for applications, then resource allocation can be planned in advance, but the estimation may result in overestimate or underestimate leading to inefficiency and misallocation
Solution Approach 1:
The system performs preliminary resource usage analysis by monitoring applications during a warm-up period before the application reaches steady state. This preliminary action captures resource usage patterns during the critical startup phase, enabling more accurate right-sizing recommendations before full production workloads begin, thus improving both planning time and estimation accuracy.
Solution Approach 2:
The system continuously monitors actual resource usage and compares it against requested resources, using this feedback loop to dynamically adjust and refine resource usage upper bounds. This feedback mechanism enables the system to learn from actual performance data and improve estimation accuracy over time while maintaining optimal resource allocation.
2Reliability
If users request more resources to ensure adequate capacity, then application performance can be maintained, but users pay for resources that they do not actually need
Solution Approach 1:
The system dynamically adjusts resource usage upper bounds based on actual monitored usage patterns rather than relying on static over-provisioning. By continuously adapting the upper bound to match actual needs, the system maintains application performance reliability while eliminating wasted resources from excessive allocation. The dynamic nature allows the system to respond to changing workload requirements in real-time.
Solution Approach 2:
The system changes the resource allocation parameter (upper bound) from a fixed over-estimated value to a dynamically adjusted value based on monitored usage. This parameter change enables the system to maintain sufficient resources for performance reliability while reducing waste by aligning allocation with actual usage patterns through continuous monitoring and adjustment.
3Loss of energy
If users request fewer resources to reduce costs, then resource efficiency improves, but application operation may be inhibited or additional fees incurred for real-time increases
Solution Approach 1:
The system performs preliminary monitoring during a warm-up period to establish accurate resource usage upper bounds before production workloads begin. This preliminary action ensures that resource allocation is optimized from the start, preventing both over-provisioning and under-provisioning that could inhibit application operation or cause additional fees for real-time increases.
Solution Approach 2:
The system implements continuous feedback monitoring to detect when actual resource usage approaches the upper bound, enabling proactive resource adjustment before performance degradation occurs. This feedback mechanism ensures application operation continuity by alerting users to potential resource constraints while maintaining efficient allocation based on actual usage patterns.
4Ease of operation
If resource allocation is fixed in advance, then resource management is simplified, but incorrect estimates result in inefficiency and unnecessary costs
Solution Approach 1:
The system enables self-service resource right-sizing by automatically monitoring application resource usage and generating right-sizing recommendations without requiring complex manual analysis. This self-service approach maintains operational simplicity while improving productivity by dynamically optimizing resource allocation based on actual usage patterns, eliminating the trade-off between simplicity and efficiency.
Solution Approach 2:
The system changes the resource allocation approach from fixed static values to dynamically adjusted parameters based on monitored usage. This parameter change allows the system to maintain simplicity of operation through automated monitoring and adjustment while significantly improving resource utilization efficiency by aligning allocation with actual needs rather than relying on manual estimation.
Data Source
AI summary
Methods, systems, and techniques for right-sizing resource requests for applications in a dynamically scalable computing environment. In one aspect, a method comprises monitoring resource usage of at least one computer resource by an application executing on a computer system, and monitoring resource requests for the computer resource(s) associated with the application. The method further comprises determining, for the computer resource(s), a resource usage upper bound associated with the application, testing the resource usage upper bound against at least one threshold, determining, from the testing, a resource request adjustment, and dynamically applying the resource request adjustment to the resource requests for the computer resource(s) associated with the application.