Federated Cloud Resource Placement Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cloud computing systems face challenges in efficiently managing and optimizing cloud resource placement and migration across federated clouds, particularly in balancing resource distribution, minimizing migration costs, addressing platform differences, and handling uncooperative cloud providers, while also predicting and preventing catastrophic VM failures and ensuring efficient rebalancing operations.
Innovation Solution
A method for cloud resource placement and migration optimization that involves unified resources monitoring, comprehensive triggering for rebalancing, smart placement optimization, and systematic migration, using a constraints-driven optimization solver to determine optimized placements and migration plans across multiple clouds, treating them as a single entity to ensure global optimization without requiring changes to existing cloud provider platforms or APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cloud resources are migrated across federated clouds to optimize resource distribution, then resource balancing and efficiency are improved, but migration costs and system complexity increase
Solution Approach 1:
The patent introduces a federated cloud management system that acts as an intermediary between multiple cloud providers and tenants. This mediator coordinates resource placement and migration across federated clouds, optimizing resource distribution while abstracting the complexity from individual cloud operations. The system manages migration costs and coordination overhead centrally, enabling efficient resource balancing without proportionally increasing overall system complexity.
Solution Approach 2:
The management system performs multiple functions including resource monitoring, optimization calculation, migration coordination, and cost management within a single unified platform. By consolidating these diverse functions into one multi-functional system, the patent reduces the need for separate specialized systems for each task, thereby optimizing resource efficiency while controlling the growth of system complexity.
2Manufacturing precision
If optimization calculations consider multiple constraints and costs across federated clouds, then resource placement accuracy is improved, but computational time and processing requirements increase
Solution Approach 1:
The system pre-calculates and stores optimization parameters, constraints, and cost metrics for resource placement before actual migration decisions are needed. By performing preliminary optimization calculations and maintaining updated placement recommendations in advance, the system reduces the computational time required during actual migration events while maintaining high placement accuracy across federated clouds.
Solution Approach 2:
The optimization problem is divided into smaller manageable segments, each handling specific constraints or cost factors independently. The system segments the complex multi-cloud optimization calculation into discrete components that can be processed separately and then combined, reducing the overall computational burden and time while maintaining comprehensive placement accuracy through the integration of segmented results.
3Productivity
If cloud providers are integrated into a federated system for global optimization, then resource allocation efficiency is improved, but control and coordination overhead increase
Solution Approach 1:
The federated cloud management system serves as a mediator that coordinates between multiple cloud providers without requiring direct complex interactions between them. This intermediary layer handles the coordination overhead centrally, enabling efficient global resource allocation while shielding individual cloud providers from the complexity of multi-provider coordination. The mediator translates global optimization goals into provider-specific actions, reducing coordination overhead.
4Reliability
If migration plans are executed systematically with ordering and enforcement mechanisms, then service continuity is improved, but operational complexity and execution time increase
Solution Approach 1:
Migration plans are pre-validated and ordered before execution begins. The system performs preliminary checks on migration feasibility, resource availability, and dependency constraints to establish an optimal execution sequence in advance. This preliminary planning ensures service continuity during migration while reducing the actual execution time by eliminating the need for complex real-time decision-making during the migration process itself.
Data Source
AI summary
The present disclosure describes a method for cloud resource placement optimization. A resources monitor monitors state information associated with cloud resources and physical hosts in the federated cloud having a plurality of clouds managed by a plurality of cloud providers. A rebalance trigger triggers a rebalancing request to initiate cloud resource placement optimization based on one or more conditions. A cloud resource placement optimizer determines an optimized placement of cloud resources on physical hosts across the plurality of clouds in the federated cloud based on (1) costs including migration costs, (2) the state information, and (3) constraints, wherein each physical host is identified in the constraints-driven optimization solver by an identifier of a respective cloud provider and an identifier of the physical host. A migrations enforcer determines an ordered migration plan and transmits requests to place or migrate cloud resources according to the ordered migration plan.