Hybrid Cloud Recommendation System for Migration Optimization
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Solution Overview
Problem
Current techniques for migrating applications to a hybrid cloud computing environment are resource-intensive and disruptive, requiring significant time and resources to identify and instantiate on-premise and off-premise resources, leading to application disruptions and inefficient use of computing and networking resources.
Innovation Solution
A recommendation system that receives application and constraint data to select objectives, correlate factors with potential data centers, apply weights to generate scores, and use preference techniques like TOPSIS to rank data centers, optimizing the hybrid cloud environment on demand without disrupting application operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current migration techniques are used to move applications to hybrid cloud environment, then the application can be executed in hybrid cloud, but significant time and resources are consumed and application disruptions occur
Solution Approach 1:
The system performs preliminary actions by pre-identifying and pre-configuring suitable data centers before application migration is needed. The recommendation system analyzes application requirements and constraint data in advance, generating a ranked list of suitable data centers that can be immediately deployed when migration is required, thus reducing actual migration time.
Solution Approach 2:
The system enables self-service by automatically analyzing application data, constraint data, and data center characteristics without requiring manual intervention. The recommendation engine autonomously correlates factors, applies weights, generates scores, and produces ranked recommendations, eliminating the need for resource-intensive manual migration planning and execution.
2Productivity
If current migration techniques are used to move applications to hybrid cloud environment, then the application can be executed in hybrid cloud, but significant computing and networking resources are consumed
Solution Approach 1:
The recommendation system performs self-service by automatically analyzing application requirements and constraint data to generate data center recommendations without requiring extensive manual computing resources. The system uses efficient algorithms to correlate factors, apply weights, and generate ranked lists, reducing the computing overhead associated with traditional migration techniques.
Solution Approach 2:
The system replaces mechanical/manual migration processes with an automated recommendation engine that uses data-driven algorithms. Instead of manually identifying and configuring data centers, the system substitutes this with automated factor correlation, scoring, and ranking mechanisms that consume fewer computing resources.
3Productivity
If current migration techniques are used to move applications to hybrid cloud environment, then the application can be executed in hybrid cloud, but application disruptions occur
Solution Approach 1:
The system performs preliminary analysis of application requirements and data center characteristics before migration occurs. By pre-identifying suitable data centers that meet all constraints and objectives, the system ensures seamless migration with minimal disruption to application continuity and reliability.
4Adaptability or versatility
If manual data center selection process is used, then flexibility in evaluating multiple objectives is achieved, but the process becomes complex and time-consuming
Solution Approach 1:
The system transforms the complex multi-objective evaluation into a standardized scoring framework where different objectives (cost, performance, sustainability, etc.) are converted into comparable numerical scores. By applying weights to correlated factors and generating composite scores, the system maintains flexibility in evaluating multiple objectives while simplifying the selection process through automated ranking.
Solution Approach 2:
The system replaces complex manual evaluation processes with automated algorithms that handle factor correlation, weight application, and score generation. This substitution maintains the ability to evaluate multiple objectives flexibly while eliminating the complexity and time-consuming nature of manual processes.
Data Source
AI summary
A device may receive application data identifying an application to be executed by a hybrid cloud computing environment and constraint data identifying constraints associated with the hybrid cloud computing environment. The device may select objectives based on the application data and the constraint data, and may identify factors associated with the objectives. The device may correlate the factors with potential data centers, and may apply weights to the correlated factors to generate correlated factor scores. The device may identify a list of data centers based on the correlated factor scores, and may determine a reduced list of data centers. The device may apply a preference technique to the reduced list of data centers to generate a ranked list of data centers, and may determine the hybrid cloud computing environment based on the ranked list of data centers. The device may perform actions based on the hybrid cloud computing environment.