Migration Wave Planning Using Analytics and Flow Modeling
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Solution Overview
Problem
Current datacenter migration methods are inefficient and labor-intensive, often resulting in suboptimal migration plans that lead to longer, riskier, and more expensive projects due to manual planning and insufficient robustness, especially in large-scale global relocation projects.
Innovation Solution
A method and system that uses analytics and flow modeling to identify workloads, determine optimization constraints, select subsets for migration, construct models representing workload relationships, and generate optimal migration wave schedules, minimizing disruptions and costs by optimizing wave sizes and frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple migration waves are used to handle large-scale workloads, then migration capacity is improved, but migration complexity increases
Solution Approach 1:
The patent segments the migration process into multiple waves, where each wave comprises a subset of workloads that can be migrated together within a single migration window. This segmentation allows large-scale migrations to be broken down into manageable chunks, enabling higher overall migration capacity while controlling complexity through systematic organization of workloads into distinct migration waves.
Solution Approach 2:
The patent performs preliminary analysis and classification of workloads before migration to determine optimal wave composition. By pre-analyzing workload dependencies, affinity relationships, and migration requirements, the system creates a structured migration plan that reduces operational complexity during execution while maximizing migration capacity through optimized wave scheduling.
2Loss of time
If migration waves are made larger to reduce project duration, then time efficiency is improved, but risk of business disruption increases
Solution Approach 1:
The patent applies different migration strategies to different workload groups based on their specific characteristics. By analyzing affinity relationships and dependency graphs, the system identifies which workloads can be migrated together and which require separate waves, allowing optimization of each local group while maintaining overall project efficiency and minimizing disruption risk.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor migration progress, workload performance, and business impact in real-time. This feedback allows the system to adjust migration wave schedules and compositions dynamically, enabling larger waves when conditions permit while reducing wave size or pausing migration when disruption risk increases, thus optimizing both time efficiency and reliability.
3Adaptability or versatility
If affinity groups are split across multiple waves, then migration flexibility is improved, but data latency increases
Solution Approach 1:
The patent implements dynamic migration wave planning that can adapt to changing conditions. The system maintains flexibility by allowing affinity groups to be split across multiple waves when necessary, while using intelligent scheduling to minimize data latency. The dynamic nature of the migration planner enables it to adjust wave compositions based on real-time workload patterns and dependency relationships.
Data Source
AI summary
A set of workloads to be migrated is identified. A constraint on optimization of the set of workloads is determined. A subset of workloads to be migrated is selected from the set of workloads. A model is constructed, using traffic information corresponding to the set of workloads. The model includes a representation of a relationship between a first workload and a second workload in the subset of workloads. The model is solved to cause generation of a set of optimal flow values. A schedule for a migration wave is constructed. The schedule complies with the constraint on optimization of the set of workloads.