Personalized Service Scheduling with Activity Forecasting
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Solution Overview
Problem
Cloud-based service providers face challenges in managing resources efficiently due to continuous service provision despite fluctuating user activity, leading to increased costs and resource wastage during inactive periods.
Innovation Solution
A machine learning model trained on signal history data from multiple users is used to predict user activity, enabling personalized service schedules that pause or disable services during inactive times, utilizing a transformer-based neural network architecture for long sequence time-series forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based services are provided continuously to ensure availability, then service reliability is improved, but resource utilization deteriorates during inactive periods
Solution Approach 1:
The service schedule is made dynamic by using machine learning predictions to automatically adjust service availability based on real-time user activity patterns. The system transitions from static continuous availability to dynamic conditional availability, enabling services to be activated or deactivated based on predicted user needs, thus resolving the contradiction between reliability and resource wastage
Solution Approach 2:
The system implements feedback loops by continuously monitoring actual user activity against predicted activity patterns. This feedback mechanism allows the machine learning model to refine its predictions and the service scheduling to optimize resource allocation, balancing service availability with resource efficiency through data-driven iterations
2Loss of energy
If personalized service schedules are implemented to optimize resource utilization, then cost reduction is achieved, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically generating and enforcing personalized service schedules based on machine learning predictions. The scheduling mechanism autonomously determines optimal service activation times without requiring manual intervention, reducing operational complexity while achieving cost reduction through automated resource optimization
Solution Approach 2:
The system manages complexity by changing operational parameters (service availability states) based on predicted user activity patterns. By transforming the service scheduling problem into parameter optimization based on machine learning outputs, the system achieves cost reduction while keeping the control logic manageable through data-driven parameter adjustments rather than complex decision frameworks
3Reliability
If machine learning models are trained on extensive user data to improve prediction accuracy, then prediction reliability is improved, but data processing requirements worsen
Solution Approach 1:
The system applies partial action by training machine learning models on selectively processed user data rather than all available data. The approach processes only the necessary portion of historical data required to achieve adequate prediction accuracy, reducing data processing volume while maintaining sufficient prediction reliability for effective service scheduling
Data Source
AI summary
The present disclosure relates to providing personalized service schedule in a computing network for a service provider to provide a service. In particular, the systems described herein utilize signal history of a plurality of users to train a model and to predict a cumulative signal amount for an individual user for a predetermined time frame in the future by drawing inferences from the model. The system described herein further transforms the predicted cumulative signal amounts to activity data and a personalized service schedule for the individual user may be updated based on the predicted activity data. The personalized service schedule may be utilized by disabling the service, or pausing the service or pausing a feature of the service when the predicted activity data indicates that the user is likely to be inactive.