Cloud Application Build Options via Usage Data Analysis
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Solution Overview
Problem
Users of cloud computing services face challenges in determining optimal deployment architectures and tracking applications and processes, leading to inefficiencies in resource usage, cost management, and downtime minimization due to the dynamic nature of cloud-based virtual machines and processes.
Innovation Solution
A decision system that monitors cloud computing environments, analyzes usage history data, and provides customized application resource build options to users, allowing for efficient resource allocation, migration between clouds, and optimal deployment configurations based on user requirements and available resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually track and monitor cloud processes and applications, then they can ensure proper operation and resource allocation, but the complexity and time required for monitoring increases significantly
Solution Approach 1:
The system enables self-service through automated monitoring and decision-making capabilities. The cloud management system automatically tracks processes, analyzes usage patterns, and generates deployment recommendations without requiring manual user intervention, thus maintaining reliability while reducing operational complexity
Solution Approach 2:
The system implements continuous feedback loops by monitoring cloud process performance, analyzing usage history data, and automatically generating recommendations. This automated feedback mechanism ensures proper operation while eliminating the need for complex manual monitoring systems
2Adaptability or versatility
If users deploy multiple applications and processes in cloud environments, then they can meet diverse functional requirements, but the difficulty of tracking and managing these processes increases
Solution Approach 1:
The system provides universal tracking and management capabilities that work across multiple cloud environments and diverse application types. The standardized monitoring approach can detect and measure various process types uniformly, making it easier to manage diverse functional requirements without increasing tracking difficulty
Solution Approach 2:
The system introduces an intermediary monitoring layer that sits between users and cloud processes. This intermediary automatically collects, aggregates, and analyzes process data, simplifying the detection and measurement of multiple applications and processes while maintaining adaptability to diverse requirements
3Productivity
If users continuously monitor cloud resources to optimize deployment, then they can minimize downtime and maximize efficiency, but the time and resources required for constant monitoring increase
Solution Approach 1:
The system performs preliminary analysis by continuously collecting and analyzing usage history data in the background. This preliminary action prepares optimization recommendations in advance, allowing users to implement efficient deployments without needing to spend time on active monitoring and decision-making
Solution Approach 2:
The system automates the optimization process by self-monitoring cloud resources and automatically generating deployment recommendations. This eliminates the need for users to invest time in constant monitoring while maintaining high resource efficiency through continuous automated optimization
Data Source
AI summary
A method of generating application build options in a cloud computing environment that includes receiving application usage data by a set of currently instantiated applications. The method further includes identifying, in view of the application usage data, a set of application resources in a cloud computing environment. The method further includes identifying additional application resources to be added to the set of application resources, to produce a modified set of application resources. The method further includes determining a cost of utilizing the modified set of application resources. The method further includes generating, in view of the cost of utilizing the modified set of application resources, a recommendation to modify the set of currently instantiated applications.