Cloud Migration Recommendation Engine for Cost Optimization
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Solution Overview
Problem
Cloud users are often unaware of resource and pricing advantages offered by new clouds, making it difficult for them to optimize their resource usage and reduce costs by switching to a more beneficial cloud.
Innovation Solution
A deployment component collects usage histories from existing cloud users and analyzes them against the resources and pricing of new clouds, generating recommendations for users who can benefit from migrating to a new cloud, using simulations and models to identify cost-efficient and resource-enhancing opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud users continue using their current cloud, then they maintain stability and familiarity with resource usage, but they miss out on potential cost reductions and resource advantages offered by new clouds
Solution Approach 1:
The system performs preliminary analysis of cloud users' usage histories and proactively identifies suitable new cloud migrations before the users decide to switch. By analyzing usage patterns, resource consumption, and cost structures in advance, the system prepares migration recommendations and cost savings projections ahead of time, enabling users to make informed decisions about transitioning to more cost-effective cloud platforms.
2Loss of energy
If cloud users switch to new clouds, then they can reduce costs and access better resource pricing, but they face complexity in evaluating and transitioning between cloud platforms
Solution Approach 1:
The system acts as an intermediary between cloud users and the complex task of evaluating new cloud platforms. It automatically analyzes usage histories, compares resource requirements against new cloud offerings, evaluates pricing structures, and generates migration recommendations. This intermediary function simplifies the migration process by handling the complex evaluation and comparison work that would otherwise burden the users.
Solution Approach 2:
The system provides continuous feedback to users about cost savings opportunities and resource matching by analyzing their usage histories. It delivers personalized recommendations showing potential cost reductions, resource advantages, and migration pathways, enabling users to make informed decisions about cloud transitions based on objective, data-driven feedback.
3Loss of energy
If cloud users manually evaluate new cloud options, then they can find cost savings, but they lack the time and expertise to properly analyze usage histories and pricing structures
Solution Approach 1:
The system performs self-service analysis by automatically processing cloud users' usage histories, comparing them against new cloud platform offerings, and generating migration recommendations without requiring manual intervention. It autonomously analyzes pricing structures, resource consumption patterns, and cost implications, freeing users from the time-consuming evaluation process while delivering personalized cost optimization recommendations.
Data Source
AI summary
Embodiments relate to systems and methods for identifying usage histories and end users that may benefit from being redeployed to a new cloud-based network. In particular, a new cloud can receive usage histories corresponding to end user usage in a respective set of other pre-existing clouds. In embodiments, the new cloud can determine whether the new cloud provides sufficient resources to properly host each end user recorded in the usage histories. Further, the new cloud can determine whether there is a cost benefit or other advantage for a user to move to the new cloud. In embodiments, a deployment recommendation may be sent to an administrator of the cloud associated with the desirable usage history.