Demand Forecasting Model for Cloud Resource Allocation
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Solution Overview
Problem
Current cloud computing systems face challenges in accurately predicting and managing web data traffic fluctuations during events, leading to inefficient resource allocation and potential unsustainable volumes of user traffic.
Innovation Solution
A method and system that generate a model using event-related features such as social media conversations, participant popularity, historical data, and event schedules to forecast future demand, determining the necessary computing resources required to meet anticipated web traffic, and automatically allocate appropriate levels of computing power to handle future data demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cloud computing systems use traditional resource allocation methods, then system simplicity is maintained, but resource allocation accuracy deteriorates leading to inefficient resource utilization
Solution Approach 1:
The system performs demand forecasting in advance by analyzing historical data and event variables before traffic spikes occur. This preliminary action enables proactive resource allocation, allowing the system to prepare appropriate computing resources ahead of time rather than reacting to demand in real-time, thus improving forecasting accuracy while maintaining manageable complexity
Solution Approach 2:
The forecasting model segments demand prediction into distinct components based on different event variables (participant popularity, social media conversations, event schedules, historical data). This segmentation allows the system to handle complex forecasting tasks through multiple specialized analyses rather than a single monolithic approach, improving accuracy without overwhelming system complexity
2Reliability
If cloud computing systems provision resources based on peak demand, then service reliability is improved, but resource utilization efficiency deteriorates due to over-provisioning during low-demand periods
Solution Approach 1:
The system dynamically adjusts resource provisioning based on forecasted demand rather than statically over-provisioning for peak scenarios. By continuously updating predictions using event variables and historical patterns, the system optimizes resource allocation to match actual demand levels, ensuring service reliability during high-demand periods while improving resource utilization efficiency during low-demand periods
Solution Approach 2:
The forecasting system incorporates feedback loops that analyze actual traffic patterns against predictions and adjust future forecasts accordingly. This feedback mechanism enables the system to learn from past performance and improve both service reliability and resource utilization efficiency by making progressively more accurate demand predictions
3Speed
If cloud computing systems react to traffic spikes in real-time, then response time is reduced, but resource allocation accuracy deteriorates due to lag in resource provisioning
Solution Approach 1:
The system performs demand forecasting in advance by analyzing historical data and event variables before traffic spikes occur. This preliminary action enables proactive resource allocation, allowing the system to prepare appropriate computing resources ahead of time rather than reacting to demand in real-time, thus improving forecasting accuracy while maintaining manageable complexity
Data Source
AI summary
An approach for forecasting demand. The approach includes a method that includes receiving one or more variables associated with an event. The method further includes generating, by at least one computing device, a model to forecast future demand based on the one or more variables. The method further includes determining, by the at least one computing device, a load to provision one or more servers to meet the future demand. The load is based on the model.