Application-Profiling Resource Scheduling for Cloud Gaming

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Solution Overview

Problem

Conventional scheduling and resource management techniques in computing systems lead to inefficiencies such as underutilization and overutilization, resulting in increased operational costs, performance bottlenecks, and reduced reliability due to imperfect demand modeling and resource allocation.

Innovation Solution

Implement user-specific and application-specific resource consumption models to predict future resource demands, allowing for proactive reallocation of resources based on user and application profiles, thereby improving demand modeling and resource allocation efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If conventional demand modeling and resource allocation techniques are used, then resource allocation can be performed, but underutilization occurs leading to inefficiency and wasted energy

Engineering Contradiction:

Improveresource utilization efficiencyVSAvoidenergy waste from underutilized hardware

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system performs preliminary actions by predicting future resource demands using machine learning models before actual demand occurs. Resource allocation is proactively adjusted based on predicted usage patterns, allowing the system to prepare resources in advance and avoid both over-provisioning and under-provisioning, thereby optimizing resource utilization efficiency while reducing energy waste from idle hardware.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements feedback mechanisms by continuously monitoring actual resource consumption and comparing it against predicted consumption. This feedback loop allows the machine learning models to be refined and updated, improving the accuracy of demand predictions over time and enabling more precise resource allocation decisions that balance utilization efficiency with energy conservation.

Inventive Principle:

Principle #23Feedback

2Productivity

If conventional demand modeling and resource allocation techniques are used, then resource allocation can be performed, but overutilization occurs leading to performance degradation and increased risk of system failures

Engineering Contradiction:

Improveresource allocation efficiencyVSAvoidsystem failure risk from overloaded components

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs preliminary resource allocation based on predicted future demand patterns rather than reacting to current demand spikes. By anticipating peak usage periods and pre-allocating resources accordingly, the system avoids sudden overutilization events that could cause performance degradation or system failures, while still maintaining efficient resource distribution during normal operation.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies beforehand cushioning by building buffer capacity into resource allocations based on predicted demand variability. This cushioning allows the system to absorb unexpected demand fluctuations without overwhelming individual components, thereby reducing the risk of system failures while maintaining high productivity through optimized resource distribution.

Inventive Principle:

Principle #11Beforehand cushioning (Prior cushioning)

3Speed

If resources are allocated based on current demand, then responsiveness to immediate needs is achieved, but latency increases due to measurement and response time

Engineering Contradiction:

Improveresponse speed to demand changesVSAvoidlatency from measurement and scaling operations

Core Design Contradiction:

SpeedVSLoss of time

Solution Approach 1:

The system eliminates latency by performing resource allocation decisions in advance based on predicted future demand patterns. Instead of measuring current demand and then reacting (which causes latency), the system uses historical data and machine learning models to predict what demand will be and allocates resources proactively, achieving both high responsiveness and minimal latency.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250284546A1Scheduling and resource management based on application profiling

Publication Date: 2025.09.11 NVIDIA CORP

AI summary

In various examples, each hosted application may be modeled with a corresponding application-specific resource consumption model that predicts a measure of that application's anticipated resource utilization at some future time based on an input representation of one or more features of the current state of an instance of the hosted application. For cloud gaming, those features may include the current level being played, current obstacles, user results playing the level or obstacles, metadata quantifying one or more aspects of the level or obstacles, game progress, etc. As such, application-specific models may be used to predict resource demands at a future time and schedule resource allocations accordingly. The present techniques may be used to manage and reallocate resources for applications such as game streaming applications, remote desktop applications, simulation applications (e.g., an autonomous or semi-autonomous vehicle simulation), virtual reality (VR) and/or augmented reality (AR) streaming applications, and/or other application types.