Application Resource Profile for Cloud Instance Scaling
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Solution Overview
Problem
Applications in computing systems face challenges due to varying resource utilization requirements over time, leading to inefficient resource allocation and potential performance issues, such as poor response times and increased costs, as actual resource usage often differs from expected values.
Innovation Solution
Implementing a method to monitor and maintain real-time computing resource utilization data, allowing for the updating of profiles that guide the selection of suitable computing environments, scaling, and migration of application instances based on actual resource usage, ensuring efficient use of resources across multiple instances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If resource allocation is based on expected values, then initial deployment is simplified, but actual resource usage efficiency deteriorates due to mismatch between expected and actual resource requirements
Solution Approach 1:
The system performs preliminary monitoring of resource usage during initial deployment phases, collecting actual resource consumption data before full-scale deployment. This preliminary action allows the system to establish accurate resource profiles early, resolving the contradiction by maintaining deployment simplicity while capturing real usage patterns for future optimization.
Solution Approach 2:
The system implements continuous feedback loops that monitor actual resource usage and update resource profiles dynamically. This feedback mechanism allows the system to learn from actual consumption patterns and adjust resource allocation accordingly, improving resource efficiency without complicating the initial deployment process.
2Measurement precision
If resource profiles are updated in real-time, then resource allocation accuracy is improved, but system complexity increases due to continuous monitoring and profile management
Solution Approach 1:
The system creates simplified copies or representations of resource usage patterns called resource profiles. Instead of managing complex real-time data directly, the system works with these abstracted profiles that capture essential resource consumption characteristics, reducing management complexity while maintaining allocation accuracy.
Solution Approach 2:
The resource profiles are designed to be dynamic rather than static, automatically adapting to changing resource usage patterns. This dynamic approach allows the system to maintain high allocation accuracy without manual intervention, reducing operational complexity despite the sophisticated monitoring capabilities.
3Reliability
If multiple application instances are deployed across computing devices, then service availability is improved, but resource management complexity increases due to varying resource requirements of different instances
Solution Approach 1:
The resource profile system serves multiple functions simultaneously: it guides initial instance deployment, monitors ongoing resource usage, enables dynamic scaling decisions, and facilitates instance migration. This universal approach to resource management simplifies the overall system architecture while maintaining service availability across multiple instances.
Solution Approach 2:
The system manages varying resource requirements by dynamically adjusting resource allocation parameters based on instance-specific profiles. Each application instance has its own resource profile with specific parameter values, allowing the system to handle diversity in resource needs without increasing overall management complexity through standardization.
Data Source
AI summary
Application lifecycle management based on real-time resource usage. A first plurality of resource values that quantify real-time computing resources used by a first instance of an application is determined at a first point in time. Based on the first plurality of resource values, one or more utilization values are stored in a profile that corresponds to the application. Subsequent to storing the one or more utilization values in the profile, it is determined that a second instance of the application is to be initiated. The profile is accessed, and the second instance of the application is caused to be initiated on a first computing device utilizing the one or more utilization values identified in the profile.