Cloud Migration Readiness via Workload Affinity Grouping
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Solution Overview
Problem
Current approaches to cloud migration are hindered by the complexity of enterprise networks, leading to costly and time-consuming efforts due to disruptions, overprovisioning, and inefficient resource allocation, as well as the challenge of determining the right-sized cloud configuration, often resulting in failed migrations and increased operational costs.
Innovation Solution
A system that discovers workload attributes, identifies dependencies among compute, network, and storage elements, groups workloads into affinity groups, determines representative synthetic workloads, and estimates cloud service provider costs, using performance analytics and cloud simulation to optimize cloud migration readiness and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If enterprise networks migrate to cloud-based infrastructure, then resource allocation efficiency improves, but network complexity and disruption risk increase
Solution Approach 1:
The patent applies preliminary action by discovering and documenting network dependencies, service ports, and communication patterns before migration occurs. The system performs pre-migration network analysis to identify all entities, their relationships, and required configurations, enabling planners to prepare migration strategies in advance and minimize disruptions during the actual migration process.
Solution Approach 2:
The patent introduces an intermediary tool that acts as a mediator between the complex enterprise network and the migration process. This discovery tool serves as an intermediary by automatically gathering network information, analyzing dependencies, and generating migration readiness reports, thereby simplifying the complex task of managing network migration without requiring manual intervention in the complex network infrastructure.
2Adaptability or versatility
If service ports are reconfigured during migration, then cloud compatibility improves, but communication disruptions increase
Solution Approach 1:
The patent applies preliminary action by discovering and documenting all service ports, their current configurations, and associated entities before migration. The system identifies required port reconfigurations in advance and plans migration sequences that minimize disruptions, allowing administrators to prepare configuration changes beforehand and execute them systematically to maintain communication reliability.
Solution Approach 2:
The patent implements feedback by continuously monitoring network entities and their communication patterns during the migration process. The discovery tool tracks service port usage and entity relationships, providing feedback information that helps administrators adjust migration plans in real-time to maintain communication reliability while achieving cloud compatibility.
3Measurement precision
If agent software is installed to track service ports, then data collection accuracy improves, but system complexity and security risks increase
Solution Approach 1:
The patent applies self-service by enabling network entities to report their own service port information and communication patterns without requiring external agent software. The discovery tool leverages built-in network capabilities and protocols to collect data autonomously, eliminating the need for additional agent installations while maintaining data collection accuracy and reducing system complexity.
4Reliability
If cloud resources are overprovisioned to ensure performance, then service reliability improves, but operational costs increase
Solution Approach 1:
The patent applies preliminary action by analyzing network entity dependencies, communication patterns, and performance requirements before migration. This pre-migration analysis enables accurate resource provisioning calculations, allowing administrators to allocate exactly the right amount of cloud resources needed for each entity, avoiding both overprovisioning and underprovisioning while ensuring service reliability.
Data Source
AI summary
A method comprising discovering workload attributes and identify dependencies, receiving utilization performance measurements including memory utilization measurements of at least a subset of workloads, grouping workloads based on the workload attributes, the dependencies, and the utilization performance measurements into affinity groups, determining at least one representative synthetic workload for each affinity group, each representative synthetic workload including a time slice of a predetermined period of time when there are maximum performance values for any number of utilization performance measurements among virtual machines of that particular affinity group, determining at least one cloud service provider (CSP)'s cloud services based on performance of the representative synthetic workloads, and generating a report for at least one of the representative synthetic workloads, the report identifying the at least one of the representative synthetic workloads and the at least one CSP's cloud services including cloud workload cost.