Social Room Resource Pre-Allocation for Predicted Live Events
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Solution Overview
Problem
Existing social networking systems face inefficiencies in allocating server resources for social rooms based on predicted future events, leading to poor quality of connection or device dropping due to unpredictable user participation, especially when pre-allocation is based on historical data rather than real-time data.
Innovation Solution
A social networking server evaluates the probability of a future event using real-time data and machine learning models, pre-allocates server resources based on subscriptions, and automatically initiates social rooms upon the event's occurrence, adjusting resource credits based on probability fluctuations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If server resources are pre-allocated based on historical data for predicted future events, then resource allocation is simplified, but resource allocation efficiency deteriorates because it cannot adapt to real-time demand changes
Solution Approach 1:
The system performs preliminary actions by creating social room templates in advance of predicted future events, pre-allocating initial server resources and configuring room parameters before the event occurs. This allows the system to have resource allocation plans ready while still adjusting final resource allocation based on real-time subscription data when the event actually happens.
2Productivity
If server resources are dynamically allocated based on real-time subscriptions, then resource allocation efficiency improves, but system complexity increases due to continuous monitoring and adjustment requirements
Solution Approach 1:
The system implements dynamic resource allocation where server resources are adjusted in real-time based on subscription counts and probability updates. The social room template configuration allows dynamic modification of resource allocation parameters as subscriptions come in, enabling the system to adapt resource allocation to actual demand without requiring complex manual intervention.
Solution Approach 2:
The system uses feedback mechanisms by continuously monitoring subscription counts and probability changes, then using this information to adjust resource allocation decisions. The machine learning model provides feedback on predicted attendance which feeds back into resource allocation calculations, creating a closed-loop system that optimizes resources based on actual observed data.
3Ease of operation
If social rooms are created ahead of time with pre-allocated resources, then user subscription convenience improves, but resource waste increases when predicted events do not occur as expected
Solution Approach 1:
The system applies partial action by pre-allocating only the minimum necessary server resources when creating social room templates, rather than allocating full resources in advance. Additional resources are allocated progressively as subscriptions come in, allowing the system to prepare rooms for users without committing excessive resources that would be wasted if the predicted event doesn't materialize.
Solution Approach 2:
The system changes resource allocation parameters dynamically based on subscription counts and probability assessments. The social room template stores configurable parameters that determine resource allocation levels, which are adjusted as real-time data becomes available. This allows the same social room infrastructure to serve different resource allocation needs based on actual demand.
4Speed
If historical data is used for resource pre-allocation, then resource allocation speed improves, but measurement precision deteriorates because historical data may not reflect current circumstances
Solution Approach 1:
The system maintains continuous useful action by combining historical data-based pre-allocation with ongoing real-time data collection and probability assessment. The social room template creation uses historical data for initial setup, then continuously updates resource allocation decisions as new subscription data and probability information becomes available, ensuring both speed and accuracy throughout the event lifecycle.
Data Source
AI summary
The present application provides for delivering initiation of social rooms for a predicted future event by evaluating a probability of the future event occurring and pre-allocating server resources of the social network platform based on the evaluated probability and subscriptions for the social room. The social networking server may receive a request, from a device of a host user, to create a social room based on a predicted future event. The social networking server may transmit an invitation to user devices on the social network to subscribe to the social room and then receive subscriptions from the user devices to join the social room. Based on an evaluated probability and the number of subscribers, the social networking server may pre-allocate particular server resources for the social room. If the future event occurs, then the social networking server initiates the social room using the pre-allocated particular server resources and automatically joins the subscribers.