Predictive Workload Migration via Site Profile Matching
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Solution Overview
Problem
Current data management systems face challenges in efficiently migrating data workloads between sites, particularly during disasters or adverse conditions, as they require manual processes that are time-consuming and prone to human error, and existing automated solutions like HyperSwap lack flexibility in spinning up new target sites based on various factors such as weather and power sources.
Innovation Solution
A computer-implemented method that compares site profiles with customer profiles based on multiple factors, including geographic and power source criteria, to generate site profiles for transferring data workloads to suitable target sites, ensuring data availability and continuity by automatically selecting sites that match customer needs and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual processes are used for migrating data workloads between sites, then flexibility in selecting target sites based on multiple factors is improved, but time consumption and human error increase
Solution Approach 1:
The system pre-generates site profiles containing multiple factors (geographic location, power sources, weather conditions, etc.) before migration is needed. When migration is required, the system quickly matches customer profiles with pre-prepared site profiles using automated factor comparison, eliminating time-consuming manual assessment while maintaining comprehensive multi-factor evaluation capability.
Solution Approach 2:
The system performs automated self-service by independently comparing site profiles with customer profiles, evaluating multiple factors, and selecting optimal target sites without human intervention. This automation maintains the flexibility of multi-factor decision-making while eliminating manual process bottlenecks and reducing time consumption.
2Loss of time
If automated solutions like HyperSwap are used for data workload migration, then time consumption is reduced, but flexibility in spinning up new target sites based on various factors is lost
Solution Approach 1:
The system segments the target site selection process into distinct evaluable factors (geographic location, power sources, weather conditions, capacity availability) represented in structured site profiles. This segmentation enables automated rapid comparison while maintaining flexibility, as each factor can be independently evaluated and weighted according to customer preferences without requiring manual intervention.
Solution Approach 2:
The system changes the approach from rigid automated failover to flexible parameter-based matching by incorporating multiple可变 parameters (geographic factors, power source types, weather conditions, capacity levels) into the migration decision. This allows automated processing to maintain flexibility by dynamically evaluating different parameter combinations against customer profiles to determine optimal target sites.
3Measurement precision
If comprehensive factor comparison is performed between site profiles and customer profiles, then accuracy in selecting suitable target sites is improved, but computational complexity increases
Solution Approach 1:
The system performs preliminary organization of comprehensive site data into structured profiles with predefined factors and metrics before comparison is needed. This pre-structuring reduces computational complexity during actual migration events by having data ready in comparable formats, while still enabling accurate multi-factor matching against customer profiles when migration is required.
Solution Approach 2:
The system creates simplified representative copies of complex site characteristics in the form of standardized site profiles. These profiles capture essential factors (geographic location, power sources, weather conditions, capacity) in a condensed format that enables accurate comparison with customer profiles without requiring processing of the full complexity of actual site infrastructure details.
Data Source
AI summary
Transferring data workloads is provided. A factor comparison is performed between each of a plurality of site profiles and a customer profile. A factor matching score is generated for each respective site profile of the plurality of site profiles based on the factor comparison. It is determined whether the factor matching score corresponding to a particular site profile is greater than or equal to a predefined minimum factor matching score threshold level. The particular site profile having the factor matching score greater than or equal to the predefined minimum factor matching score threshold level is selected as a recommended site profile for transferring a data workload of a customer to a set of target sites from a current site running the data workload. The recommended site profile is sent to a client device corresponding to the customer via a network.