Cloud Gaming Resource Allocation via User Demand Prediction
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Solution Overview
Problem
Cloud gaming systems face issues with resource misallocation, leading to prolonged game waiting times and resource wastage due to inadequate allocation of computing resources.
Innovation Solution
A method that collects current and historical online user data to predict future user demand, allowing for dynamic adjustment of cloud computing resources allocated to preloading game clients, ensuring optimal resource utilization and reducing waiting times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If cloud computing resources are increased to reduce game waiting time, then user experience is improved, but resource wastage increases
Solution Approach 1:
The patent implements dynamic resource allocation by continuously monitoring online user quantities in real-time and adjusting cloud computing resource allocation accordingly. The system transitions from static to dynamic resource management, where resource allocation changes adaptively based on current and historical user data, ensuring resources are allocated efficiently without excessive wastage while maintaining reduced waiting times.
Solution Approach 2:
The patent applies preliminary action by predicting future online user quantities based on historical data and current trends. This prediction enables the system to pre-allocate computing resources before peak demand occurs, reducing game waiting time proactively rather than reactively, while avoiding over-allocation during low-demand periods.
2Device complexity
If cloud computing resources are allocated based on fixed rules, then resource allocation is simple, but resource misallocation occurs
Solution Approach 1:
The patent implements feedback mechanisms by continuously collecting and analyzing online user quantity data from historical periods and current time. This feedback loop enables the system to adjust resource allocation dynamically based on actual usage patterns, significantly improving allocation accuracy compared to fixed rules, while the automated nature of the feedback process keeps complexity manageable.
Solution Approach 2:
The system performs self-service by automatically predicting user demand and adjusting resource allocation without manual intervention. The cloud server autonomously analyzes historical data, predicts future online quantities, and allocates resources accordingly, reducing the need for complex manual management while improving allocation reliability through data-driven decisions.
Data Source
AI summary
A resource allocation method and apparatus, a readable medium, an electronic device and a program product are provided. The resource allocation method includes: collecting a current online user quantity of a cloud game in a current time period, and counting an online user quantity of the cloud game in a historical time cycle; predicting a target online user change quantity of the cloud game in a future time period according to the current online user quantity and the online user quantity of the cloud game in the historical time cycle, the future time period being a time period having a same time length as the current time period; and dynamically adjusting, according to the target online user change quantity, cloud computing resources allocated to the cloud game, preloading a game client being based on the cloud computing resources