Cloud Application Optimization via Full-Stack Visibility and Dynamic Adjustment
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Solution Overview
Problem
The complexity of managing cloud applications in public cloud environments poses challenges due to limited visibility and control over virtualized resources and services, making it difficult to achieve performance, availability, and security Service Level Objectives (SLOs) compared to traditional on-premise infrastructure.
Innovation Solution
A computerized method for optimizing cloud application performance involves building a full-stack view, providing an application model, mapping performance needs to cloud resources, detecting issues, dynamically adjusting application layers, and determining real-time aggregate costs to ensure SLOs are met while optimizing resource allocation and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional on-premise management approaches are used for cloud applications, then operational control and visibility are maintained, but the ability to scale and adapt to cloud environments is limited
Solution Approach 1:
The patent introduces a cloud application management platform as an intermediary layer between traditional operational approaches and cloud infrastructure. This platform provides visibility and control mechanisms specifically designed for cloud environments, enabling operators to manage distributed applications effectively while maintaining adaptability to cloud dynamics.
Solution Approach 2:
The management approach is segmented into multiple layers including application layer, infrastructure layer, and optimization layer. Each layer handles specific management concerns independently, allowing operational control at the application level while adapting to cloud infrastructure changes without direct intervention.
2Reliability
If cloud resources are dynamically allocated to meet performance SLOs, then application performance is improved, but resource consumption and costs increase
Solution Approach 1:
The system dynamically adjusts cloud resource allocation based on real-time monitoring of application performance metrics and SLO compliance. Resources are allocated flexibly, scaling up when performance thresholds are approached and scaling down when SLOs are comfortably met, optimizing the balance between reliability and resource efficiency.
Solution Approach 2:
The patent implements continuous feedback loops that monitor application performance, SLO compliance, and resource consumption. This feedback drives automated optimization decisions, adjusting resource allocation in response to actual performance needs rather than static provisioning, thereby reducing unnecessary resource consumption while maintaining SLO compliance.
3Measurement precision
If full visibility and control over cloud resources are implemented, then performance optimization is enabled, but system complexity increases
Solution Approach 1:
The management platform provides universal visibility and control mechanisms that work across diverse cloud resources and services through standardized interfaces. This multi-functional approach consolidates monitoring, management, and optimization capabilities into a single system, reducing overall complexity while maintaining comprehensive visibility.
Solution Approach 2:
The platform acts as an intermediary layer that abstracts the complexity of individual cloud resources and services. It provides unified visibility and control through standardized mechanisms, shielding operators from underlying infrastructure complexity while enabling precise measurement and optimization of cloud resource usage.
4Ease of manufacture
If traditional monolithic application architecture is used, then development and deployment are simpler, but scalability and agility are reduced
Solution Approach 1:
The patent manages segmented, distributed application architectures where applications are divided into independent deployable units. This segmentation enables flexible deployment of individual components, improving scalability and agility while the management platform provides unified control to maintain operational simplicity.
Solution Approach 2:
The management approach enables partial deployment and scaling of application components based on specific needs. Rather than deploying entire monolithic applications, operators can selectively deploy and scale individual services or functions, achieving scalability without requiring complete architectural redesign.
Data Source
AI summary
A computerized method for optimizing cloud application performance, including the step of monitoring of a cloud application. The method includes the step of building a full-stack view of the cloud application. The method includes the step of providing an application model. The method includes the step of mapping one or more cloud application performance needs to a set of cloud-resources based on the application model. The method includes the step of detecting a performance problem with the cloud application. The method includes the step of dynamically adjusting a specified layer of the cloud application to meet an application performance SLO. The method includes the step of, as cloud resources are consumed, determining a real-time aggregate cost for a specified application operation.