Workload Deployment via Dynamic Resource Configuration

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Solution Overview

Problem

In computing networks, workloads often remain unexecuted due to the unavailability of interoperable resources, as existing methods rely on manual or automated determination of interoperability, which limits deployment and migration capabilities.

Innovation Solution

The described techniques allow workload deployment by identifying and modifying the configuration of resources, using a deployment engine to select resources based on rating parameters such as utilization, migration, and configuration changes, enabling deployment even when interoperable resources are unavailable, and facilitating migration between non-interoperable resource sets.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If manual or automated determination of interoperability is used, then resource compatibility can be assessed, but workload deployment is limited when interoperable resources are unavailable

Engineering Contradiction:

Improveworkload deployment capabilityVSAvoidresource interoperability assurance

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent modifies resource configuration parameters dynamically to achieve interoperability. When a workload cannot be deployed on available resources due to interoperability constraints, the system changes configuration parameters of the resources (such as software versions, firmware, or operational modes) to make them compatible with the workload requirements, thereby expanding deployment capabilities without compromising reliability

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system transitions from static resource configuration to dynamic configuration adjustment. Resources can adapt their configuration states based on workload requirements and compatibility assessments, allowing the system to dynamically resolve interoperability issues and enable workload deployment that would otherwise be blocked by fixed resource characteristics

Inventive Principle:

Principle #15Dynamics

2Adaptability or versatility

If resource configuration is modified to enable workload deployment, then deployment flexibility improves, but system complexity increases

Engineering Contradiction:

Improvedeployment flexibilityVSAvoidconfiguration management complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system implements self-service through automated configuration modification. Rather than requiring manual intervention to adjust resource configurations for compatibility, the system autonomously assesses interoperability requirements and performs necessary configuration changes, reducing the operational complexity burden on users while maintaining high deployment flexibility

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system employs feedback mechanisms to manage configuration complexity. By continuously monitoring resource states, compatibility outcomes, and workload deployment status, the system uses this feedback information to make intelligent configuration adjustments, preventing uncontrolled complexity growth while achieving flexible deployment across diverse resource configurations

Inventive Principle:

Principle #23Feedback

3Productivity

If rating-based resource selection is implemented, then workload deployment efficiency improves, but computational overhead increases

Engineering Contradiction:

Improveworkload deployment efficiencyVSAvoidcomputational energy consumption

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by implementing selective rating-based evaluation. Rather than comprehensively evaluating all possible resource configurations for every workload, the system focuses rating computations on the most promising or relevant resource subsets, achieving sufficient deployment efficiency while limiting excessive computational energy consumption through targeted rather than exhaustive assessment

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS10432548B2Workload deployment in computing networks

Publication Date: 2019.10.01 HEWLETT PACKARD ENTERPRISE DEV LP

AI summary

Techniques of workload deployment in a computing network are described. For example a computing system may receive a workload deployment request for deployment of a workload on resources of the computing network. The computing system may determine unavailability of interoperable resources for deployment of the workload, based on interoperability information associated with each of the resources, and identify at least one set of resources for deployment of the workload, where deploying the workload on each set of resources from amongst the at least one set of resources comprises changing configuration of at least one resource included within corresponding set of resources. The computing system may further rate each of the set of resources based on deployment parameters, and select a first set of resources from amongst the at least one set of resources for deployment of the workload based on the rank of each of the set of resources.