Cloud Migration Pricing and Sequence Optimization
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Solution Overview
Problem
Cloud migration is challenging due to the complexity of estimating per-application migration pricing and determining the optimal sequence of application movement to the cloud, considering factors like application dependency and compatibility, which often leads to underestimated costs and inefficient migration processes.
Innovation Solution
A method and system utilizing optimization techniques such as neural networks, forest poly tree optimization, fuzzy ant colony optimization, and triangular fuzzy optimization, along with pre-defined priorities, to estimate per-application migration pricing and optimize the application move group sequence for cloud migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional migration estimation methods are used, then the migration process is simple, but the cost estimation accuracy deteriorates
Solution Approach 1:
The patent segments the migration cost estimation into multiple independent modules: application dependency analysis module, migration cost calculation module, and optimization module. Each module handles specific aspects of the estimation process separately, making the complex overall system manageable while improving accuracy through specialized processing of different cost factors.
Solution Approach 2:
The patent performs preliminary actions by first analyzing application dependencies and identifying migration groups before calculating actual migration costs. The system pre-determines migration sequences and groups applications based on dependencies, which allows for more accurate cost estimation later without requiring complex real-time calculations during the actual migration process.
2Productivity
If application migration sequence is optimized considering all dependencies, then migration efficiency improves, but the complexity of determining the sequence increases
Solution Approach 1:
The patent divides the application migration sequence into distinct groups based on dependency relationships. Applications are segmented into migration groups where each group contains applications with similar dependency patterns, allowing the system to determine sequences for each group independently and then combine them, reducing overall complexity while maintaining optimization.
Solution Approach 2:
The patent implements a dynamic sequence determination approach where the migration sequence is not fixed but adapts based on analyzed dependencies. The system uses optimization algorithms that can dynamically adjust the migration sequence based on the specific dependency graph of applications, allowing efficient sequencing without requiring complex manual intervention for each scenario.
3Measurement precision
If per-application pricing is estimated using detailed optimization techniques, then cost accuracy improves, but the time required for estimation increases
Solution Approach 1:
The patent performs preliminary analysis of application characteristics, dependencies, and migration requirements before final cost estimation. By pre-processing and categorizing applications into groups with similar migration profiles, the system reduces the time required for detailed per-application pricing calculations while maintaining accuracy through the structured approach.
Solution Approach 2:
The patent changes the parameters used in cost estimation based on application categories and dependency groups. Instead of using the same detailed optimization parameters for all applications, the system adjusts parameters according to the specific characteristics of each application group, which reduces estimation time for homogeneous applications while maintaining precision through parameter adaptation.
Data Source
AI summary
This disclosure relates generally to cloud migration strategy and, more particularly, to estimation of per-application migration pricing and application move group sequence for cloud migration. The per-application migration pricing is the cost incurred to migrate an application from an organization's infrastructure to a cloud and the application move group sequence is an optimized application movement sequence from an organization's infrastructure to a cloud. The method and system propose to estimate the per-application pricing for cloud based on a set of optimization techniques and neural networks. Further the application move group sequence for cloud migration is estimated based on a forest poly tree optimization, a fuzzy ant colony optimization, a triangular fuzzy optimization and a plurality of pre-defined priorities.