Cloud Deployment Engine for Selective Workload Migration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud management platforms lack tools to automatically analyze and decide on selective workload migration or federation based on workload conditions, leading to potential over-subscription and increased costs due to manual decision-making.
Innovation Solution
A cloud deployment engine that tracks resource consumption across multiple users and clouds, using selection criteria to determine whether to migrate workloads to federated backup clouds or new hosts, optimizing resource allocation and cost management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual evaluation is used to decide on workload migration or backup, then decision flexibility is maintained, but decision-making time increases and cost optimization is reduced
Solution Approach 1:
The system enables self-service by implementing an automated decision-making engine that independently evaluates workload conditions, compares cloud options, and executes migration or backup decisions without requiring manual intervention. The engine uses predefined selection criteria and automatically monitors resource consumption patterns to trigger appropriate actions, thereby eliminating decision-making delays while preserving operational flexibility through configurable parameters.
Solution Approach 2:
The system applies preliminary action by pre-configuring selection criteria, cost parameters, and migration policies before workload issues arise. The automated engine has predetermined rules for evaluating cloud performance, cost-effectiveness, and workload suitability, allowing it to make immediate decisions when triggered without requiring real-time manual analysis, thus reducing decision-making time while maintaining flexibility through pre-set options.
2Reliability
If complete migration to new cloud hosts is performed, then resource over-subscription is eliminated, but migration complexity and costs increase
Solution Approach 1:
The system applies segmentation by dividing the workload into migratable and non-migratable components, and by segmenting the migration process into discrete steps. The automated engine evaluates individual workloads and determines partial migration strategies, migrating only necessary portions to new cloud hosts while leaving critical components on original hosts. This reduces overall migration complexity while ensuring resource stability for critical functions.
Solution Approach 2:
The system implements partial action by performing selective migration of only those workloads that meet specific criteria for migration benefit, rather than migrating all workloads. The automated engine identifies and migrates only the portion of workloads where migration provides clear advantages in cost or performance, leaving other workloads on original hosts. This approach eliminates over-subscription issues for critical workloads while reducing overall migration complexity and cost.
3Loss of energy
If federated backup clouds are used for temporary deployment, then cost is reduced, but deployment duration is limited
Solution Approach 1:
The system applies dynamics by implementing dynamic workload placement that automatically adjusts between federated backup clouds and primary cloud hosts based on real-time conditions. The automated engine continuously monitors workload performance, resource availability, and cost parameters, dynamically migrating workloads between cloud environments. This allows the system to utilize cost-effective federated backup clouds during appropriate periods while automatically transitioning to primary hosts when longer duration or higher performance is required, optimizing both cost and deployment duration.
4Measurement precision
If automated analysis tools are implemented, then decision accuracy improves, but system complexity increases
Solution Approach 1:
The system applies universality by implementing a multi-functional automated engine that consolidates multiple analysis functions into a single platform. The engine simultaneously performs workload characterization, cloud option evaluation, cost analysis, performance prediction, and decision execution. This universal approach improves measurement precision through comprehensive analysis while reducing overall system complexity by eliminating the need for separate specialized tools for each analysis function.
Data Source
AI summary
Embodiments relate to systems and methods for a cloud deployment engine for selective workload migration or federation based on workload conditions. A set of aggregate usage history data can record consumption of processor, software, or other resources subscribed to by one or more users in a or clouds. An entitlement engine can analyze the usage history data to identify a subscription margin and other trends or data reflecting short-term consumption trends. An associated deployment engine can analyze the short-term consumption trends, and generate a decision to either deploy any over-subscribed resources to a set of federated backup clouds, or to one or more new host clouds. In aspects, the decision to augment the capacity of the host cloud with either a cloud federation or a complete host cloud replacement can be based on a set of selection criteria, including the margin by which the resources are over-subscribed and/or whether the over-subscription is static, increasing or accelerating, among others.