Runtime Engine Selection for Low-Latency Application Deployment

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Solution Overview

Problem

Cloud service providers face challenges in maintaining consistent service levels and cost-effectiveness, while container-based virtualization systems lack efficient methods for deploying and managing virtual machine workloads, leading to potential resource inefficiencies and increased deployment latencies.

Innovation Solution

A method and system that utilize a cluster manager to examine application configuration data and integration runtime engine profiles, evaluate suitability, and deploy applications to the most suitable runtime engine, thereby avoiding redundant resource installations and optimizing resource utilization.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If applications are deployed to new runtime engines with all required resources installed, then application deployment reliability is improved, but resource utilization efficiency deteriorates due to redundant installations

Engineering Contradiction:

Improveapplication deployment reliabilityVSAvoidresource utilization efficiency

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary examination of application configuration data and integration runtime engine profile data before deployment, evaluating suitability in advance to identify existing runtime engines that can support the application without requiring new resource installations

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables existing runtime engines to serve multiple applications by evaluating their suitability for different workloads, allowing a single runtime engine to support multiple applications that share common resource requirements, thereby avoiding redundant resource installations

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of energy

If applications are deployed to existing runtime engines, then resource utilization efficiency is improved, but deployment time increases due to evaluation processes

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoiddeployment time

Core Design Contradiction:

Loss of energyVSLoss of time

Solution Approach 1:

The system performs preliminary evaluation of runtime engine suitability by examining application configuration data and integration runtime engine profile data before deployment, identifying compatible runtime engines in advance to avoid deployment delays

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system enables runtime engines to self-evaluate their suitability for applications by comparing their installed resources against application requirements, allowing automated decision-making without manual intervention and reducing overall deployment time

Inventive Principle:

Principle #25Self-service

3Reliability

If cloud service levels are maintained through traditional cloud deployment, then service availability is improved, but cost effectiveness deteriorates

Engineering Contradiction:

Improveservice availabilityVSAvoidcost effectiveness

Core Design Contradiction:

ReliabilityVSEase of manufacture

Solution Approach 1:

The system merges multiple application workloads onto shared runtime engines by evaluating their compatibility and resource requirements, consolidating infrastructure usage to reduce costs while maintaining service availability through intelligent workload placement

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The system changes deployment parameters by selecting runtime engines based on evaluated suitability metrics rather than fixed assignment rules, optimizing the balance between service availability and cost effectiveness through dynamic parameter adjustment

Inventive Principle:

Principle #35Parameter changes

4Loss of energy

If container-based virtualization is used, then resource footprint is reduced, but deployment management complexity increases

Engineering Contradiction:

Improveresource footprintVSAvoiddeployment management complexity

Core Design Contradiction:

Loss of energyVSDevice complexity

Solution Approach 1:

The system implements feedback mechanisms by continuously examining runtime engine profile data and application configuration data, evaluating suitability and monitoring deployment outcomes to automatically adjust deployment decisions, thereby simplifying management of container-based virtualization environments

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250362895A1Application deployment

Publication Date: 2025.11.27 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining application configuration data defining a configured software application and integration runtime engine profile data that specifies attributes of a plurality of integration runtime engines currently running within a computer environment; evaluating, in dependence on the examining, a suitability of respective ones of the plurality of the integration runtime engines for supporting running of the configured software application; returning an action decision in dependence on the evaluating, wherein the action decision specifies a selected integration runtime engine for supporting running of the configured software application; and deploying the configured software application to the selected integration runtime engine.