Intent-Based Auto Scaling for Virtual Computing Resources
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Solution Overview
Problem
Service provider networks face challenges in ensuring predictable and efficient scaling of virtual computing resources due to capacity limitations and variability in resource availability, leading to inconsistent performance and unsatisfied user demands.
Innovation Solution
Implementing intent-based auto scaling configurations that allow users to specify primary and secondary scaling settings, including resource types, priority, and timing, enabling the auto scaling service to generate a fulfillment plan that aligns resource allocation with user intentions and available capacity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional auto scaling is used to automatically provision and de-provision compute instances, then capacity management is automated to some degree, but scaling predictability and performance consistency deteriorate due to capacity limitations and variability in resource availability
Solution Approach 1:
The system performs preliminary actions by predicting future resource requirements based on historical data and patterns before actual scaling events occur. This allows the auto scaling system to proactively provision resources in advance, ensuring capacity availability and consistent performance without waiting for threshold breaches or load spikes.
Solution Approach 2:
The system implements dynamic scaling policies that adapt to changing workloads and resource availability in real-time. By continuously monitoring multiple metrics and adjusting scaling decisions dynamically, the system maintains reliability and predictability while fully automating the scaling process, resolving the contradiction between automation extent and scaling reliability.
2Reliability
If aggressive scaling policies are implemented to ensure sufficient compute capacity, then resource availability improves, but resource waste and cost increase due to over-provisioning during low-demand periods
Solution Approach 1:
The system continuously monitors multiple metrics including resource utilization, workload patterns, and scaling performance to provide real-time feedback. This feedback loop enables the system to learn from past scaling decisions and adjust future provisioning strategies, optimizing the balance between ensuring resource availability and preventing over-provisioning waste through data-driven adjustments.
Solution Approach 2:
The system dynamically adjusts scaling parameters such as threshold values, scaling rates, and target capacities based on observed workload patterns and system performance. By changing these parameters adaptively rather than using fixed aggressive policies, the system maintains resource availability when needed while reducing provisioning during low-demand periods, thereby minimizing resource waste.
3Measurement precision
If multiple scaling metrics are monitored to improve scaling accuracy, then scaling precision improves, but system complexity increases due to additional monitoring and processing requirements
Solution Approach 1:
The system employs a unified multi-functional monitoring framework that simultaneously tracks multiple metrics including CPU utilization, memory usage, network traffic, and custom business metrics. This universal monitoring approach improves scaling accuracy by considering diverse factors while avoiding the complexity of separate monitoring systems through integrated data collection and analysis capabilities.
Solution Approach 2:
The system introduces an intermediary layer that aggregates and normalizes data from multiple monitoring sources before processing. This intermediary component simplifies the complexity by providing a standardized interface for metric collection, filtering, and correlation, enabling accurate multi-metric monitoring without proportionally increasing system complexity through modular architecture.
4Speed
If fast scaling is implemented to respond quickly to demand changes, then responsiveness improves, but system stability deteriorates due to scaling oscillations and downstream service impacts
Solution Approach 1:
The system implements beforehand cushioning by implementing damping mechanisms and rate limiting in the scaling logic. These protective measures prevent excessive scaling oscillations and buffer the impact of rapid scaling events on downstream services, maintaining system stability while preserving responsive scaling through controlled adjustment rates and predictive smoothing.
Solution Approach 2:
The system performs preliminary scaling actions based on predicted demand trends before actual load spikes occur. This proactive approach enables faster effective response to demand changes while maintaining stability by avoiding reactive oscillations, as resources are provisioned in advance based on forecasts rather than reacting to already-occurred load changes that could cause instability.
Data Source
AI summary
Techniques are described for enabling users to configure the auto scaling of virtual computing resources using various intent-based auto scaling configurations. Intent-based configurations generally provide various indications of how a user intends to use a scalable group of virtual computing resources and what expectations a user might have about how those resources are scaled in response to changes in demand. A service provider network receives first input to create a scalable group of virtualized computing resources, the first input indicating at least one primary scaling configuration used by the hardware virtualization service to scale the scalable group of compute instances over time, and further receives second input indicating at least one secondary, intent-based scaling configuration used to scale the virtualized computing resources, for example, when the service is unable to scale the group of virtualized computing resources according to the at least one primary scaling condition.