Dynamic Resource Scaling via Closed-Loop Feedback Control
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Solution Overview
Problem
Current automated scaling methods for network computing resources require customer intervention and configuration, and are not suitable for time-sensitive applications due to the time it takes for resources to become available, often failing to accommodate sudden spikes in demand.
Innovation Solution
A dynamic scaling system using a closed-loop feedback control mechanism that adjusts resources based on customer-provided parameters, such as CPU, memory, and network utilization, allowing for instantaneous responses to changes in demand by automatically provisioning or de-provisioning resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If rule-based or schedule-based auto-scaling mechanisms are used, then automated scaling is achieved, but customer intervention and configuration are required which increases operational complexity
Solution Approach 1:
The system enables self-service by allowing customers to define high-level objectives and constraints without requiring configuration of specific scaling rules. The autonomous controller automatically interprets these objectives, monitors system state, and executes scaling decisions without customer intervention, transforming manual rule configuration into automated goal-based control.
Solution Approach 2:
The system implements feedback mechanisms where the autonomous controller continuously monitors system performance metrics, compares them against customer-defined objectives, and adjusts resource allocation accordingly. This closed-loop feedback eliminates the need for manual rule configuration by automatically adapting to changing system conditions.
2Productivity
If traditional automated scaling methods are used, then resources can be scaled, but it takes several minutes for resources to become available which is not suitable for time-sensitive applications
Solution Approach 1:
The system performs preliminary actions by pre-configuring resource templates and maintaining a pool of available computing resources that can be instantly deployed. When scaling is needed, the autonomous controller immediately allocates pre-prepared resources rather than undergoing lengthy provisioning processes, enabling sub-minute deployment times for time-sensitive applications.
Solution Approach 2:
The system introduces dynamic scaling capabilities where resource allocation decisions are made in real-time based on current system state and customer objectives. The autonomous controller dynamically adjusts resource allocation without fixed delays, enabling rapid response to sudden demand changes while optimizing resource usage based on actual performance metrics.
3Adaptability or versatility
If traditional automated scaling methods are used, then scaling rules can be configured, but the system cannot respond instantly to unanticipated traffic spikes or immediate response needs
Solution Approach 1:
The system transitions from static, pre-configured scaling rules to dynamic, real-time adaptive scaling. The autonomous controller continuously monitors system metrics and automatically adjusts resource allocation in response to changing conditions, enabling instant response to traffic spikes without relying on pre-defined time-based or threshold-based rules.
Solution Approach 2:
The system implements real-time feedback loops where the autonomous controller continuously monitors system performance, compares it against customer objectives, and immediately executes scaling decisions. This feedback mechanism enables the system to respond instantly to unanticipated demand changes without the delays inherent in traditional rule-based approaches.
Data Source
AI summary
Current methods for providing automated scaling of network resources require tracking a specific metric and based on that metric exceeding a specified limit, provisioning additional resources. By providing additional control functionality for enabling customers to select parameters to use for automated resource scaling, customer systems can automatically and dynamically receive additional resources based on the selected parameters.