Virtual Machine Resource Pool Scaling Through Event Feedback
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Solution Overview
Problem
The manual determination of resource pool size for software processes often leads to suboptimal results, either resulting in decreased performance due to insufficient resources or wasted memory, as the size is fixed and not dynamically adjusted based on actual usage patterns.
Innovation Solution
A system that automatically adjusts the size of a resource pool for software processes based on detected events, using a scaling policy to increase or decrease the number of resource objects in response to specific event types, allowing for real-time optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the resource pool size is manually determined and fixed, then the system configuration is simple and stable, but the performance decreases due to insufficient resources or wasted memory
Solution Approach 1:
The patent implements dynamic adjustment of resource pool size based on event notifications from virtual machines. The system transitions from a static, manually-determined resource pool configuration to a dynamic one that automatically scales up or down in response to runtime conditions, thereby improving performance without requiring complex manual intervention
Solution Approach 2:
The system employs feedback mechanisms by monitoring event notifications from virtual machines and using this information to adjust resource pool size. The feedback loop continuously observes system state and makes automated adjustments, resolving the contradiction between simple configuration and optimal performance
2Productivity
If the resource pool size is increased to ensure sufficient resources, then performance improves, but memory is wasted due to over-provisioning
Solution Approach 1:
The resource pool size is dynamically adjusted based on actual usage patterns detected through event notifications. Instead of statically over-provisioning memory, the system adapts the resource pool size in real-time, ensuring sufficient resources for high performance while avoiding memory waste during low-demand periods
Solution Approach 2:
The system changes the parameter of resource pool size based on observed usage patterns. By monitoring events and adjusting the number of resource objects accordingly, the system optimizes the balance between having enough resources for performance and avoiding memory waste through precise parameter adaptation
3Adaptability or versatility
If the resource pool size is manually configured, then the system is easy to operate, but it cannot adapt to changing usage patterns
Solution Approach 1:
The system enables self-service by allowing the resource pool to automatically adjust its own size based on event notifications from virtual machines. This eliminates the need for manual reconfiguration while maintaining adaptability to changing usage patterns, thereby preserving ease of operation while gaining adaptability
Solution Approach 2:
Through feedback from event notifications, the system automatically detects changing usage patterns and adjusts resource pool size accordingly. This feedback-driven adaptation maintains operational simplicity while achieving high adaptability to varying workloads
Data Source
AI summary
Events associated with a virtual machine can be used to adjust an amount of resource objects that may be allocated from a resource pool for the virtual machine. For example, a system can receive a notification including event data indicating an event related to the virtual machine, wherein the virtual machine is configured to rely on resource objects in a resource pool for performing one or more computing operations. The system can determine that the event is a particular type of event. Based on determining that the event is the particular type of event, the system can select a scaling policy corresponding to the particular type of event. The system can then apply the scaling policy to the event data to determine a scaling strategy for adjusting the amount of resource objects.