Resource Usage Profiles for Accurate Application Instance Allocation
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Solution Overview
Problem
Existing computing environments lack the ability to accurately determine the computing resources required by applications, leading to inefficient allocation and reduced performance due to overallocation or underallocation of resources, affecting both the instance of the application and other applications running on the same device.
Innovation Solution
The system generates resource usage profiles based on real-time monitoring of application instances under different operating conditions, allowing for the selection of appropriate computing devices and virtual machine resource allocations to ensure sufficient resources are allocated.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If application instances are initiated without resource usage profiles, then the initiation process is simple and fast, but resource allocation is inefficient leading to insufficient or excessive resources being allocated
Solution Approach 1:
The system performs preliminary actions by generating resource usage profiles beforehand through monitoring and analyzing historical resource consumption patterns. These profiles are created in advance and stored for later use when initiating new application instances, allowing the system to quickly reference pre-analyzed data rather than performing resource analysis at initiation time.
Solution Approach 2:
The system creates simplified copies of resource usage information in the form of resource usage profiles that capture essential consumption patterns. These profiles serve as lightweight representations that can be quickly referenced during instance initiation, avoiding the need to perform complete resource analysis from scratch for each new instance.
2Measurement precision
If resource usage profiles are generated and used for instance initiation, then resource allocation accuracy is improved, but system complexity increases
Solution Approach 1:
The system implements self-service by automatically monitoring resource usage, generating profiles, and using them for instance initiation decisions. The system manages its own resource allocation process without requiring manual intervention or complex external control mechanisms, reducing overall system complexity while maintaining high allocation accuracy.
Solution Approach 2:
The system establishes feedback loops where resource usage is continuously monitored and fed back into the profile generation process. This automated feedback mechanism allows the system to continuously improve resource allocation accuracy by learning from actual usage patterns, while the automated nature of the feedback process prevents complexity from escalating.
3Adaptability or versatility
If multiple resource usage profiles are generated for different operating conditions, then resource allocation adaptability is improved, but data processing requirements increase
Solution Approach 1:
The system segments resource usage data by different operating conditions, creating separate resource usage profiles for distinct operational contexts. This segmentation allows the system to match instances with appropriate profiles based on current conditions, improving adaptability while keeping each individual profile manageable in size and processing requirements.
Data Source
AI summary
A plurality of resource usage profiles is generated, wherein at least two of the resource usage profiles each include a corresponding plurality of resource values that quantify real-time computing resources used by an instance of an application that previously executed under two different corresponding operating conditions. It is determined that a new instance of the application is to be initiated. A particular resource usage profile from the plurality of resource usage profiles is selected. The new instance is initiated using the particular resource usage profile.