Cloud VM Migration for Stranded Resource Recovery
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Solution Overview
Problem
Cloud computing environments suffer from resource fragmentation due to stranded resources caused by resource bottlenecks, leading to inefficiencies, increased power consumption, and potential VM allocation failures.
Innovation Solution
A system that measures resource utilization within cloud computing environments to identify nodes with stranded resources, migrates VMs to alleviate bottlenecks, and proactively recovers stranded resources by intelligently selecting candidate nodes and VMs for migration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If VMs are allocated to nodes based on available resources, then resource allocation is simplified, but resource fragmentation occurs causing stranded resources
Solution Approach 1:
The system dynamically monitors resource utilization metrics and automatically triggers VM migration when stranded resources are detected. The resource management system adapts its behavior based on real-time conditions, transitioning from static allocation to dynamic optimization, thereby resolving the contradiction between allocation simplicity and resource utilization efficiency.
Solution Approach 2:
The system implements a feedback mechanism that continuously measures resource utilization, identifies stranded resources, and triggers corrective actions (VM migration). This closed-loop control ensures that resource allocation decisions are based on actual utilization patterns, improving overall resource efficiency while maintaining operational simplicity through automation.
2Productivity
If VM migration is performed to recover stranded resources, then resource utilization efficiency improves, but system complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the resource management system automatically detects stranded resources and initiates VM migration without human intervention. The system serves itself by monitoring its own resource utilization and taking corrective actions, thereby improving resource efficiency while managing complexity through automation rather than manual processes.
Solution Approach 2:
The system changes operational parameters (VM placement, resource allocation ratios) based on measured resource utilization metrics. By dynamically adjusting these parameters in response to detected stranded resources, the system optimizes resource utilization efficiency while the parameter-based approach provides a structured method for managing system complexity.
3Productivity
If resource monitoring and VM migration are implemented, then stranded resources are reduced, but power consumption increases
Solution Approach 1:
The system implements periodic resource monitoring and triggers VM migration only when stranded resources are detected, rather than continuously operating. This periodic action reduces power consumption by keeping the monitoring system dormant during normal operation and activating only when optimization opportunities arise, thereby balancing resource utilization improvement with energy efficiency.
Solution Approach 2:
The system performs preliminary resource utilization analysis before triggering VM migration, identifying stranded resources in advance. By taking preliminary action to assess whether migration is necessary, the system avoids unnecessary migration operations that would consume additional power, thereby improving resource efficiency while minimizing energy overhead.
Data Source
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AI summary
The present application is directed to stranded resource recovery in a cloud computing environment. A resource utilization signal at each of a plurality of nodes that each hosts corresponding virtual machines (VMs) is measured. Based on each resource utilization signal, a set of candidate nodes is identified. Each candidate node comprises a stranded resource that is unutilized due to utilization of a bottleneck resource. The identification includes calculating an amount of the stranded resource at each candidate node. From a plurality of VMs hosted at the set of candidate nodes, a set of candidate VMs is identified for migration for stranded resource recovery. The identification includes calculating a score for each candidate VM based on a degree of imbalance between the stranded resource and the bottleneck resource at a candidate node hosting the candidate VM. Migration of at least one candidate VM in the set of candidate VMs is initiated.