Hybrid Multi-Cloud Workload Forecasting for Proactive Resource Provisioning

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Solution Overview

Problem

In modern computing environments, predicting workload demands and resource allocation is challenging due to the complexity of systems, delays in resource initialization, and the need for tailored resource provisioning across hybrid cloud and on-premises setups, leading to inefficiencies and user experience degradation.

Innovation Solution

A system utilizing machine learning models, generative artificial intelligence, and retrieval augmented generation to predict short- and long-term resource usage patterns, enabling personalized provisioning plans with user interaction and transparency, optimizing resource allocation to minimize delays and costs.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If resources are allocated on a completely ad-needed basis when users request them, then user experience is improved by providing resources exactly when needed, but resource initialization delays cause degraded user experience as users must wait while resources are being initialized

Engineering Contradiction:

Improveuser experienceVSAvoidresource initialization delay

Core Design Contradiction:

Ease of operationVSLoss of time

Solution Approach 1:

The system performs preliminary actions by predicting future resource needs using machine learning models and proactively provisioning resources before users actually request them. The workload forecasting system analyzes historical data and predicts when resources will be needed, allowing the system to initialize and prepare resources in advance, eliminating the wait time that would otherwise occur during initialization.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If more resources are prepared in advance to reduce wait times, then user experience is improved, but resource idle time increases causing waste of substantial sums of money

Engineering Contradiction:

Improveuser experienceVSAvoidresource idle time cost

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system implements feedback mechanisms where machine learning models continuously analyze actual resource usage patterns, user behavior, and workload trends to refine predictions. This feedback loop allows the system to adjust its forecasting accuracy over time, provisioning resources more precisely when needed and avoiding both over-provisioning (which causes idle time waste) and under-provisioning (which causes wait times).

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system dynamically changes provisioning parameters based on predicted workload patterns. Instead of using fixed provisioning rules, the system adjusts parameters such as resource quantity, timing, and type based on machine learning predictions of future demand. This allows optimal balancing of proactive resource allocation against idle time minimization.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If a machine learning model is used to predict resource usage patterns, then resource allocation is optimized, but system complexity increases

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The machine learning model acts as an intermediary component that sits between resource usage data and provisioning decisions. Rather than implementing complex optimization algorithms directly in the resource allocation system, the ML model processes historical data and outputs predictions that simplify the provisioning logic. This intermediary approach manages system complexity by encapsulating predictive intelligence in a separate, manageable module.

Inventive Principle:

Principle #24Intermediary (Mediator)

4Productivity

If resources are allocated based on predicted usage patterns, then resource allocation is optimized and costs are minimized, but accuracy of predictions becomes challenging due to changing workload patterns

Engineering Contradiction:

Improveresource allocation optimizationVSAvoidprediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The system implements dynamic adaptation to changing workload patterns through continuous learning. The machine learning models are retrained periodically with new data to account for changing user behaviors, seasonal variations, and emerging workload patterns. This dynamic approach allows the system to maintain prediction accuracy despite evolving conditions, balancing optimization benefits with adaptability to change.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260072755A1Workload forecasting and planning in hybrid multi-cloud computing environments

Publication Date: 2026.03.12 SAP SE

AI summary

A machine learning model is trained to predict short-and long-term resource usage for a user and to take these predictions and forms a provisioning plan for one or more resources, optimized such that resources are started in enough time before they are needed to be used to reduce or eliminate any appreciable delay on the user experience-side but not too much before they are needed that the resource(s) would be idle waiting for usage to occur. If a Gen AI control center is provided that offers a comprehensive user interface displaying the current system state, suggested plans, and prediction quality measures, users can then interact with a chatbot in the Gen AI control center for detailed information on selected plans, and manually add events to anticipate demand spikes. The Gen AI control center is able to create explanatory text and diagrams, improving the understanding of suggested plans.