Cloud Gaming Resource Allocation via Online Interaction Prediction
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Solution Overview
Problem
Cloud gaming systems face challenges in optimally allocating resources to ensure a satisfactory gaming experience for users, as the demand for resources is difficult to predict, leading to either over-allocation and wastage or under-allocation, which affects the quality of service and incurs unnecessary expenses.
Innovation Solution
A resource allocation model using machine learning algorithms processes online interactions, including in-game and off-game interactions, to predict resource usage and adjust resource provisioning accordingly, scaling up or down resources in anticipation of usage spikes across different geo-locations and sub-systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud gaming systems over allocate resources to ensure satisfactory gaming experience, then service quality is improved, but resource wastage increases and expenses increase
Solution Approach 1:
The system performs preliminary actions by analyzing online interactions (social media posts, forum discussions, in-game communications) to predict future game usage spikes before they occur. This allows the resource allocation model to proactively scale resources up in advance of actual demand, ensuring service quality is maintained without the need for continuous over-allocation. The machine learning model processes interaction data to identify patterns that precede usage spikes, enabling timely resource adjustment.
Solution Approach 2:
The resource allocation model dynamically adjusts resource provisioning based on real-time and historical interaction data. Instead of static over-allocation, the system continuously monitors online interactions and automatically scales resources up or down according to predicted demand. This dynamic approach allows the system to maintain high service quality during usage spikes while reducing resource wastage during low-demand periods, directly resolving the contradiction between reliability and energy loss.
2Loss of energy
If cloud gaming systems allocate resources optimally to reduce wastage, then expenses are reduced, but difficulty in determining demand increases
Solution Approach 1:
The system introduces online interactions as an intermediary indicator to indirectly measure and predict actual game usage demand. Instead of directly measuring difficult-to-predict usage patterns, the model analyzes easier-to-capture interaction data (social media posts, forum discussions, in-game communications) that serve as leading indicators of upcoming usage spikes. This intermediary approach simplifies demand detection while maintaining high resource allocation accuracy.
Solution Approach 2:
The patent replaces traditional mechanical demand detection methods (direct usage monitoring, manual analysis) with an automated machine learning system that processes online interactions. The ML model automatically correlates interaction patterns with usage spikes, substituting complex manual demand assessment with an automated predictive system that reduces both the difficulty of demand detection and resource wastage simultaneously.
3Ease of manufacture
If cloud gaming systems use traditional resource allocation methods, then implementation is simple, but resource utilization is suboptimal and expenses increase
Solution Approach 1:
The resource allocation model operates autonomously, processing online interactions and automatically adjusting resource provisioning without requiring manual intervention or complex configuration. The machine learning system self-trains on interaction data and self-adjusts resource allocation decisions, making the sophisticated approach as easy to implement as traditional methods while dramatically improving resource utilization efficiency and reducing expenses.
Data Source
AI summary
Methods and systems for provisioning resources for games executed by a cloud gaming system includes accessing online interactions of a plurality of users in relation to a game. The online interactions are processed to classify discussion features obtained from online social communications. A model is generated to predict game use, using the classified discussion features. The model is updated with online interactions received over time. Resources for the game are provisioned at a data center. The provisioning is done by accessing the model and identifying adjustments in the provisioning for an anticipated usage spike that is to occur by a plurality of users, based on current online interactions.