Dynamic Instance Selection for Game Hosting
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Solution Overview
Problem
The high cost of dedicated resource capacity for hosting online multiplayer games can be prohibitive for game developers, and the risk of interruptions with spot capacity is a concern due to the potential for sudden reclamation, which can negatively impact the gaming experience.
Innovation Solution
A system that dynamically allocates resource capacity by selecting instance types based on risk scores and cost, utilizing spot capacity that is not currently in use by other customers, with a placement manager determining the likelihood of resource reclamation and optimizing for low risk and cost, while ensuring minimal latency and high availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dedicated resource capacity is used to host game sessions, then reliability and availability are improved, but cost increases significantly
Solution Approach 1:
The system dynamically selects between spot instances and reserved instances based on real-time risk assessment and game session requirements. The placement manager continuously evaluates risk scores and adjusts instance selection strategies, transitioning from static capacity planning to dynamic resource allocation that adapts to changing conditions while managing both cost and availability.
Solution Approach 2:
The system changes the parameter of instance selection from fixed (dedicated) to variable (risk-based selection). By introducing risk scores as a dynamic parameter that influences instance type selection, the system can adjust between spot and reserved instances based on acceptable risk levels, thereby optimizing the cost-availability tradeoff.
2Quantity of substance
If spot capacity is used to host game sessions, then cost is reduced, but reliability deteriorates due to potential sudden reclamation
Solution Approach 1:
The placement manager implements a feedback mechanism by calculating risk scores based on historical data and current conditions, then using these scores to inform instance selection decisions. This closed-loop system continuously learns from outcomes and adjusts placement strategies, enabling the system to navigate the spot instance reclamation risk while maintaining cost efficiency.
Solution Approach 2:
The system performs preliminary risk assessment and instance selection before game sessions begin. By pre-evaluating risk scores and selecting appropriate instance types in advance, the system prepares mitigation strategies ahead of time, reducing the impact of potential spot instance reclamation during active gaming sessions.
3Reliability
If risk assessment is performed for instance selection, then reliability is improved, but device complexity increases
Solution Approach 1:
The placement manager performs self-service by automatically calculating risk scores and selecting instances without manual intervention. The system uses historical data and current conditions to autonomously make placement decisions, reducing the need for complex manual management processes while maintaining high reliability through systematic risk assessment.
Data Source
AI summary
New application sessions can be placed on reclaimable resource capacity that can be provided at lower cost than dedicated capacity, but with risk that the capacity might be reclaimed before completion of the session. A number of instance types can be determined that are capable of hosting a new session. Risk scores can be calculated for the instance types, as well as the cost of instances of each type. An instance type can be selected for the session using the risk scores and cost information, where the instance type will have relatively low risk with a relatively low cost for the instance. Once an instance type is selected, a resource pool can be selected from which to provide the resource instance of the selected type. The instance can then be allocated and caused to host the session or perform related functionality.