Dynamic Host Ordering Policy for Cloud VM Placement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cloud computing environments face inefficiencies in optimizing physical hosts for virtual machines due to limited flexibility in placement policies, which restricts dynamic adaptation to changing conditions and resource utilization.
Innovation Solution
An ordering policy mechanism that allows system administrators to create and select both fixed and dynamic ordering policies, enabling the optimizer to dynamically change the order of optimization for physical hosts based on various conditions such as cost, utilization, and service level agreements, thereby optimizing host prioritization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed placement policies are used to determine physical host optimization order, then policy simplicity is maintained, but adaptability to changing conditions deteriorates
Solution Approach 1:
The patent implements dynamic ordering policies that automatically adjust the optimization sequence of physical hosts based on real-time conditions such as resource utilization, cost metrics, and service level agreements. This transforms static placement policies into dynamic systems that adapt to changing cloud environment conditions, resolving the contradiction between policy simplicity and adaptability.
Solution Approach 2:
The system changes the parameters used for determining optimization order from fixed values to variable parameters that respond to current system state. By introducing parameters such as current resource utilization, cost per cycle, and SLA compliance status, the policy can adapt its behavior based on changing conditions while maintaining a structured decision-making framework.
2Adaptability or versatility
If dynamic ordering policies are implemented to adapt to changing conditions, then adaptability improves, but system complexity increases
Solution Approach 1:
The patent segments the optimization process into distinct phases: evaluation phase (assessing current host conditions), ordering phase (determining optimization sequence based on multiple criteria), and execution phase (performing migrations). This segmentation allows the complex dynamic policy to be managed through modular, manageable components rather than a monolithic complex system.
Solution Approach 2:
The patent introduces an intermediary ordering policy mechanism that sits between the placement policy and the optimization execution. This intermediary layer processes multiple input factors (cost, utilization, SLA) and translates them into a prioritized optimization sequence, simplifying the overall system architecture by centralizing the decision-making logic in a dedicated component.
3Productivity
If optimization order is fixed, then processing time is reduced, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent performs preliminary evaluation of host conditions and pre-determines optimization priorities before actual migration execution. By assessing resource utilization, cost metrics, and SLA requirements in advance and establishing an optimized execution sequence, the system prepares the optimization plan beforehand, enabling efficient execution without real-time decision-making delays during the actual migration process.
Data Source
AI summary
Dynamically setting the order of optimization of physical hosts allows more efficient and varied optimization. An ordering policy mechanism utilizes ordering policies to set an order for the optimizer to optimize physical the hosts. The ordering policy mechanism may allow a system administrator to create and/or select ordering policies. The ordering policies may include fixed ordering policies or dynamic ordering policies.