Client Device Allocation Algorithm for Cloud Gaming Servers
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Solution Overview
Problem
Cloud-based gaming systems face challenges in efficiently allocating client devices to data centers and servers to maintain a satisfactory user experience while optimizing resource utilization, as initial allocation is critical due to long-running sessions and high resource demands, and reassignment during gameplay can disrupt the gaming experience.
Innovation Solution
An allocation algorithm and bidding process are used to determine resource-efficient allocation of client devices to data centers and servers based on various criteria, including real-time and historical data, to optimize resource usage and user experience, with periodic reevaluation for potential reassignment during gameplay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If client devices are assigned to data centers using a simple allocation method, then the allocation process is fast and easy to implement, but resource utilization is inefficient and user experience is not optimized
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing multiple criteria (network latency, server load, user preferences, geographic location) before making allocation decisions. The allocation algorithm pre-evaluates multiple data centers and prepares optimized assignment recommendations before final allocation, ensuring efficient resource utilization from the start.
Solution Approach 2:
The allocation system implements feedback mechanisms by continuously monitoring allocation outcomes, user experience metrics, and resource utilization data. This feedback is fed back into the allocation algorithm to refine future allocation decisions, improving resource efficiency while maintaining manageable complexity through iterative optimization.
2Productivity
If client devices are reassigned during gameplay to optimize resource utilization, then resource efficiency improves, but gameplay continuity is disrupted
Solution Approach 1:
The system applies dynamics by implementing conditional reassignment strategies that adapt to gameplay state. Reassignment is dynamically enabled only during appropriate moments (e.g., between levels, during loading screens, or when gameplay interruption is minimal), allowing resource optimization while preserving gameplay continuity through state-aware decision-making.
Solution Approach 2:
The allocation system uses periodic action by evaluating reassignment opportunities at specific intervals or triggers rather than continuously. Reassignment is considered periodically based on gameplay milestones, server load thresholds, or scheduled maintenance windows, enabling resource optimization without constant disruption to gameplay.
3Reliability
If multiple criteria are considered for allocation, then user experience and resource efficiency are optimized, but the allocation process becomes more complex
Solution Approach 1:
The allocation system applies segmentation by dividing the complex multi-criteria evaluation into distinct modular components. Each criterion (network latency, server load, user preferences, geographic location) is evaluated as a separate module with its own scoring function, making the overall system more manageable and easier to implement while still considering all factors comprehensively.
4Speed
If allocation decisions are made in real-time, then user experience is optimized, but computational resources are consumed
Solution Approach 1:
The system performs preliminary action by pre-calculating and caching allocation recommendations based on historical data and current system state. When real-time allocation decisions are needed, the system retrieves pre-prepared recommendations rather than performing full multi-criteria analysis, achieving fast real-time responses while reducing computational resource consumption through advance preparation.
Data Source
AI summary
Systems and methods are provided for optimizing allocation of client devices, such as gaming devices, to different available data centers and servers, such as gaming servers, within a data center. The allocation maintains a satisfactory user experience while balancing costs and resource usage. Embodiments of the invention match a client to a data center and server based on a set of criteria related to the client device, user of the client device, game or application the user desires to play, available data centers for supporting the client device, and conditions of the network between the client device and data center. An allocation algorithm may be used to determine resource-efficient allocation. In one embodiment, a bidding process is employed wherein a client and data center are matched based on bids received from data centers. In one embodiment, allocation is periodically evaluated to determine if reassignment to another data center is needed.