Multitenant Cloud Resource Visualization and Planning
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Solution Overview
Problem
Migrating legacy software applications to cloud environments is challenging due to compatibility issues, inefficient resource utilization, and the need to adapt to varying cloud configurations, requiring effective matching and comparison of local and cloud resources to ensure cost-effectiveness, scalability, and security.
Innovation Solution
The use of a scanning engine to identify application and cloud characteristics, a partitioning engine to divide workloads into executable groups, and a mapping engine to assign these groups to appropriate cloud resources, along with a rendering engine for visual representation and optimization, facilitates efficient workload deployment and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If legacy software applications are migrated to cloud environments, then scalability and accessibility are improved, but compatibility issues and inefficient resource utilization occur
Solution Approach 1:
The patent segments the monolithic legacy application into multiple microservices or functional modules that can be independently deployed and managed in the cloud environment. This segmentation allows each component to be optimized for cloud compatibility while maintaining overall system functionality, resolving the contradiction between scalability and compatibility.
Solution Approach 2:
The patent introduces an intermediary layer or adapter that sits between the legacy application and the cloud environment, translating and mediating interactions to ensure compatibility. This intermediary handles protocol conversions, data format transformations, and interface adaptations, allowing legacy applications to run efficiently in cloud environments without direct modification.
2Ease of operation
If cloud resources are allocated to support legacy applications, then accessibility is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent creates a universal cloud platform or containerization system that can host multiple legacy applications and modern applications simultaneously using shared infrastructure. This multi-functionality approach allows a single cloud resource pool to serve diverse workloads efficiently, improving both accessibility and resource utilization by eliminating dedicated hardware requirements for each application.
3Measurement precision
If comprehensive scanning and analysis of applications is performed before migration, then migration accuracy is improved, but migration time increases
Solution Approach 1:
The patent performs preliminary scanning, analysis, and assessment of the legacy application architecture, dependencies, and resource requirements before the actual migration process. This preliminary action includes generating migration roadmaps, identifying compatibility issues, and preparing transformation scripts in advance, which significantly reduces the time required during the actual migration execution while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual, time-consuming analysis processes with automated scanning tools, AI-driven assessment systems, and script-based dependency analysis. These automated systems rapidly analyze application codebases, configuration files, and runtime behaviors to generate comprehensive migration assessments, dramatically reducing analysis time while improving precision through systematic algorithmic evaluation.
Data Source
AI summary
A system for migrating a non-tenant-aware local application to a tenant-aware cloud application environment is disclosed to migrate individual modules of the application to instances of the cloud by grouping the modules via common characteristics in partition groups. By grouping modules together by partition group before migrating the modules to cloud instances, modules that share resources can be placed in closer logical proximity to one another in the cloud, modified, or deleted to optimize performance. The information from these modules is processed to create a visualization graph representing information on the cloud. The visualization graph is preferably multilayered so it can display information from different hierarchical layers of a cloud instance.