Predictive Cloud Resource Scaling via Historical Demand Analysis
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Solution Overview
Problem
Current provisioning technologies for computing resources, such as those used in cloud services, react slowly to increased demand, leading to delays in scaling up resources during peak times and resulting in either inadequate service or wasteful over-provisioning during low traffic periods, which is costly and inefficient.
Innovation Solution
Implement predictive provisioning logic that uses historical data and state data to anticipate upcoming resource needs, proactively requesting additional resources before they are actually needed, allowing sufficient time for them to become operational and adjusting resources according to specific constraints, such as re-provisioning limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If reactive provisioning is used to match computing resources to current needs, then resource allocation efficiency is improved, but response time to handle peak load deteriorates
Solution Approach 1:
The system performs preliminary actions by predicting future resource needs using historical data and machine learning models, then proactively provisions resources before the actual demand occurs. This allows resources to be ready in advance, eliminating the minutes-long delay inherent in reactive provisioning while maintaining efficiency by only provisioning predicted needed resources.
2Reliability
If over-provisioning is used to ensure adequate resources during peak load, then service availability is improved, but resource waste and cost increase
Solution Approach 1:
The system dynamically changes resource provisioning parameters based on predicted demand rather than using static over-provisioning. Machine learning models analyze historical patterns and predict future resource needs, allowing the system to adjust provisioning levels precisely to match expected demand, ensuring availability during peaks while avoiding waste during low-traffic periods.
3Reliability
If predictive provisioning is implemented, then resource availability during peak times is improved, but system complexity increases
Solution Approach 1:
The system implements self-service through automated machine learning models that independently analyze historical data, predict future resource needs, and trigger provisioning actions without manual intervention. This automation handles the complexity internally while presenting a simple interface for ensuring resource availability, balancing improved reliability with manageable system complexity.
Data Source
AI summary
The described technology is generally directed towards predicting a need for provisioned (e.g., cloud computing) resources so that the provisioned resources are proactively provisioned and operational before their actual need. Historical data is used to predict upcoming resource demand so that resources begin to be scaled up to meet demand in advance, at a prediction time, given that there is a “spin-up” delay between requesting resources and their actual availability. The predictive resource provisioning is adaptable to override customary historical data with expected exceptions, and is flexible to handle variable spin-up times, constraints, and optimizations.