Virtual Machine-Aware Query Optimizer for Hibernated VMs

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Solution Overview

Problem

Database management systems face performance challenges in processing queries due to heavy workloads, especially when virtual machines are hibernated, deactivated, or offline, leading to inefficiencies in resource utilization and query execution.

Innovation Solution

A virtual machine-aware query optimizer that determines burden values for asset actions such as activation, deployment, migration, or circumvention of virtual machines to establish an optimal query plan, ensuring efficient query execution even when virtual machines are not in a desired state.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Use of energy by moving object

If virtual machines are hibernated or deactivated to save computing resources, then resource usage efficiency is improved, but query execution performance deteriorates

Engineering Contradiction:

Improvecomputing resource usageVSAvoidquery execution speed

Core Design Contradiction:

Use of energy by moving objectVSSpeed

Solution Approach 1:

The system proactively identifies queries that can be executed on hibernated virtual machines and prepares execution plans in advance. By detecting queries against hibernated VMs and determining burden values for activation actions before execution, the system ensures query performance is maintained while minimizing unnecessary VM activations, thus resolving the contradiction between resource savings and query execution speed.

Inventive Principle:

Principle #10Preliminary action

2Speed

If virtual machines are activated or migrated to process queries, then query execution performance is improved, but computing resource overhead increases

Engineering Contradiction:

Improvequery execution speedVSAvoidcomputing resource overhead

Core Design Contradiction:

SpeedVSLoss of energy

Solution Approach 1:

The system introduces burden values as a quantitative parameter to evaluate the cost of activating or migrating virtual machines. By calculating burden values that factor in VM state, resource availability, and query characteristics, the query optimizer can make informed decisions about whether activation or migration is warranted, thus improving query performance while minimizing unnecessary resource overhead.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements a feedback mechanism where burden values are calculated based on current VM states and resource conditions, and this information feeds back into query plan selection. The query optimizer uses this feedback to choose between executing queries on active VMs, activating hibernated VMs, or migrating to different physical compute nodes, thereby optimizing the balance between execution speed and resource overhead.

Inventive Principle:

Principle #23Feedback

3Reliability

If the system activates hibernated virtual machines for query processing, then availability of data is improved, but system complexity increases

Engineering Contradiction:

Improvedata availabilityVSAvoidsystem management complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system implements self-service mechanisms where the query optimizer automatically detects hibernated virtual machines, calculates burden values for activation, and executes queries without manual intervention. The system autonomously manages the complexity of tracking VM states, determining activation needs, and coordinating with the virtualization layer, thereby improving data availability while keeping system management complexity transparent to users.

Inventive Principle:

Principle #25Self-service

4Productivity

If query plans are optimized considering virtual machine states, then resource utilization efficiency is improved, but query plan management complexity increases

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidquery plan management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The burden value calculation mechanism serves multiple functions: it evaluates VM activation costs, assesses migration options, and guides query plan selection. This universal approach consolidates what would otherwise require separate complex management systems for each scenario, thereby improving resource utilization efficiency while managing query plan complexity through a unified framework.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS11487760B2Query plan management associated with a shared pool of configurable computing resources

Publication Date: 2022.11.01 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Disclosed aspects relate to query plan management associated with a shared pool of configurable computing resources. A query, which relates to a set of data located on the shared pool of configurable computing resources, is detected. A virtual machine includes the set of data. With respect to the virtual machine, a set of burden values of performing a set of asset actions is determined. Based on the set of burden values, a query plan to access the set of data is established. Using at least one asset action of the set of asset actions, the query plan is processed.