Virtual Machine Memory Increment Selection
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Solution Overview
Problem
Current memory reclamation in virtual machines is inefficient, as it often starts from the highest memory address and fails if a memory increment cannot be removed, leading to prolonged processes and potential system bottlenecks due to a lack of suitable increment selection for removal.
Innovation Solution
A method is introduced to select memory increments for reclamation based on failure counts and available frames, prioritizing those with the least failures and most free space, ensuring efficient memory reconfiguration and avoiding stuck reclamation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If memory reclamation starts from the highest memory address, then the process follows a simple sequential approach, but it fails if a memory increment cannot be removed, leading to prolonged processes and system bottlenecks
Solution Approach 1:
The system performs preliminary actions by calculating failure counts for each memory increment before attempting reclamation. This advance preparation allows the system to identify and prioritize increments with lower failure counts, avoiding stuck processes and improving overall reclamation efficiency without complicating the execution phase.
Solution Approach 2:
The invention changes the selection parameter from a fixed sequential order (highest address first) to a dynamic parameter based on failure counts. By monitoring and using failure count data to guide increment selection, the system adapts to actual memory conditions, preventing bottlenecks while maintaining process simplicity.
2Productivity
If memory increments are selected for removal without considering failure history, then the selection process is simple and fast, but reclamation may fail and require prolonged attempts
Solution Approach 1:
The system implements feedback by tracking failure counts for each memory increment based on previous reclamation attempts. This feedback mechanism allows the system to learn from past failures and use that information to make smarter selection decisions, improving success rates while maintaining selection speed through efficient data structures.
Solution Approach 2:
Failure counts are calculated and prepared in advance before reclamation operations begin. This preliminary computation of failure statistics enables the system to quickly query and select optimal increments without performing complex analysis during the actual reclamation process, thus maintaining speed while improving reliability.
3Quantity of substance
If the system attempts to vacate memory increments with high failure counts, then more memory can be reclaimed, but the process becomes prolonged and may cause system bottlenecks
Solution Approach 1:
The system applies local quality by treating different memory increments differently based on their individual failure counts. Instead of a uniform approach, increments with lower failure counts are prioritized for reclamation, while those with high failure counts are deferred. This localized differentiation optimizes both the quantity reclaimed and the time required by focusing efforts on the most promising targets.
Data Source
AI summary
An aspect of the invention includes a method for receiving a request to reclaim a portion of a memory assigned to a virtual machine (VM). In response to receiving the request, an increment of the plurality of increments to vacate is selected. The selecting is based at least in part on the failure counts corresponding to each of the plurality of increments. An attempt is made to vacate all contents of the selected increment. Based at least in part on determining that all contents of the selected increment were not vacated, a failure count corresponding to the selected increment is incremented. Based at least in part on determining that all contents of the selected increment were vacated, an assignment of the selected increment to the VM is removed.