Cloud Migration Workload Partitioning and Resource Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Migrating legacy software applications to cloud environments is challenging due to compatibility issues, inefficient resource utilization, and cost considerations, as existing technologies fail to effectively match application requirements with cloud resources and optimize deployment strategies.
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, enables efficient and cost-effective 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 accessibility and scalability are improved, but compatibility issues and inefficient resource utilization occur
Solution Approach 1:
The system segments the legacy application into multiple workloads that can be independently mapped to cloud resources. The workload analyzer divides the application into discrete functional units, allowing selective migration and compatibility testing of individual segments rather than the entire application at once.
Solution Approach 2:
The patent introduces a cloud migration planning system as an intermediary between the legacy application and cloud environment. This intermediary analyzes application requirements, generates workload mappings, and creates contingency plans, serving as a mediator that resolves compatibility issues before deployment.
2Productivity
If cloud resources are allocated to legacy applications, then cost-effective scalability is improved, but resource utilization efficiency deteriorates
Solution Approach 1:
The system dynamically maps workloads to cloud resources based on actual application requirements and resource availability. The workload analyzer continuously evaluates resource usage patterns and adjusts mappings to optimize utilization, allowing the system to adapt to changing demands rather than using static allocations.
Solution Approach 2:
The patent changes the parameters of resource allocation by using detailed workload characteristics (CPU, memory, storage, network requirements) to match applications with appropriate cloud resources. This parameter-based matching ensures efficient resource utilization while maintaining scalability.
3Measurement precision
If comprehensive cloud environment analysis is performed, then migration planning accuracy is improved, but time and computational resources required increase
Solution Approach 1:
The system performs preliminary analysis by pre-identifying application workloads, their dependencies, and resource requirements before actual migration. The workload analyzer creates a detailed profile of the legacy application in advance, generating contingency plans and resource mappings beforehand to streamline the migration process.
Solution Approach 2:
The cloud migration planning system performs self-service analysis by automatically analyzing application characteristics, generating workload mappings, and creating migration plans without requiring extensive manual intervention. This automation reduces both time and computational resources while maintaining high accuracy.
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.