Memory Access Tracking for Pre-fetching in Virtualization

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Solution Overview

Problem

Existing virtualization systems lack efficient data pre-fetching mechanisms that adapt to memory usage patterns, leading to suboptimal operational efficiency and potential operational issues such as malware detection and resource management challenges.

Innovation Solution

Implementing a predictive model that utilizes a memory access tracking data structure to anticipate and pre-fetch memory blocks based on observed usage patterns, enhancing operational efficiency and enabling diagnostics for potential issues.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If data pre-fetching is implemented without adaptive memory usage patterns, then data availability may improve, but system complexity and resource consumption increase

Engineering Contradiction:

Improvedata availabilityVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-fetching data blocks into the buffer cache before they are actually requested by virtual machines. The predictive model identifies memory blocks that are likely to be accessed next and initiates data retrieval in advance, improving data availability while managing complexity through pattern-based prediction rather than exhaustive analysis

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring memory access patterns and using the predictive model to adjust pre-fetching decisions. The model learns from observed usage patterns and refines predictions over time, allowing the system to adapt to changing workloads and maintain optimal performance without requiring manual configuration or excessive complexity

Inventive Principle:

Principle #23Feedback

2Measurement precision

If comprehensive memory access tracking is implemented, then prediction accuracy improves, but processing overhead and memory consumption increase

Engineering Contradiction:

Improveprediction accuracyVSAvoidprocessing overhead

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by tracking and analyzing only the most significant memory access patterns rather than every single access event. The predictive model focuses on identifying recurring patterns and frequently accessed memory blocks, achieving sufficient prediction accuracy without the prohibitive overhead of comprehensive tracking of all memory operations

Inventive Principle:

Principle #16Partial or excessive action

Solution Approach 2:

The system changes parameters by adjusting the granularity and depth of memory access tracking based on workload characteristics. The predictive model can adapt its analysis depth and tracking intensity dynamically, modifying how it monitors memory patterns to balance accuracy requirements against processing overhead for different operational scenarios

Inventive Principle:

Principle #35Parameter changes

3Productivity

If pre-fetching is performed for all virtual machines simultaneously, then overall system performance improves, but resource contention and management complexity increase

Engineering Contradiction:

Improvesystem performanceVSAvoidresource management flexibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The system segments the pre-fetching operation by virtual machine and memory access pattern type. Each virtual machine's predictive model operates independently, analyzing its own memory access patterns and generating pre-fetch requests specific to its workload. This segmentation allows parallel operation of multiple predictive models while maintaining resource management flexibility through isolated control of each virtual machine's pre-fetching behavior

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements dynamic resource allocation for pre-fetching operations. The predictive models continuously adapt to changing memory access patterns and adjust pre-fetching intensity based on current system conditions and available resources. This dynamic approach allows the system to optimize performance for each virtual machine while automatically adapting to resource constraints and contention scenarios

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS11886709B1Pre-fetching data based on memory usage patterns

Publication Date: 2024.01.30 PARALLELS INT GMBH

AI summary

Systems and methods for pre-fetching data based on memory usage patterns. An example method comprises: receiving a first memory access request identifying a first memory block; receiving a second memory access request identifying a second memory block; update a memory access tracking data structure by incrementing a sequence counter corresponding to a memory access sequence that references the first memory block and the second memory block; receive a third memory access request identifying a third memory block; identifying, based on the memory access tracking data structure, a sequence counter having a maximal value among sequence counters associated with memory access sequences that reference the third memory block; and pre-fetching a fourth memory block corresponding to the identified sequence counter.