Predictive Autoscaling for Cloud Compute Resource Allocation
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Solution Overview
Problem
Existing cloud computing resource allocation methods are either manual and non-adaptive or reactive, leading to inefficiencies such as delayed resource allocation and deallocation, which can fail to mitigate short-term load changes effectively.
Innovation Solution
A predictive autoscaling system that analyzes historical compute metrics to detect seasonal patterns and trends, allowing for proactive allocation or deallocation of resources based on predicted future behavior, ensuring adequate resources are available before peak demands and reducing unnecessary resource usage during lows.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive autoscaling is used to allocate resources based on detected resource consumption, then resource allocation responds to actual demand, but the inherent delay causes failure to mitigate short-term load changes effectively
Solution Approach 1:
The system performs preliminary actions by allocating resources in advance based on predicted future resource consumption. The autoscaler analyzes historical metrics to forecast future demand and proactively scales resources before the predicted peak occurs, eliminating the reactive delay. This is implemented by generating predictions for future time frames and initiating scale operations ahead of the predicted demand surge.
2Ease of operation
If manual scheduling rules are used to allocate computing resources at predetermined times, then resource allocation follows a fixed schedule, but the system is not adaptive to real life scenarios and is difficult to tune and maintain
Solution Approach 1:
The system implements feedback by continuously analyzing actual resource consumption metrics and comparing them with predicted values. The autoscaler uses this feedback to refine its predictions and adjust future scaling decisions. Historical metrics are constantly ingested and used to update the prediction model, making the system adaptive to changing patterns without manual intervention.
Solution Approach 2:
The system performs self-service by automatically analyzing its own performance metrics and making autonomous scaling decisions based on predictions. The predictive autoscaler independently forecasts future resource needs and executes scaling operations without requiring manual rule configuration or tuning, eliminating the operational complexity of manual scheduling while maintaining high adaptability.
3Reliability
If additional compute resources are allocated ahead of predicted metric exceeding threshold, then resource availability is ensured during peak demand, but resource utilization efficiency may be compromised if predictions are inaccurate
Solution Approach 1:
The system applies partial action by allocating resources based on the degree of predicted demand increase. Rather than always allocating maximum resources, the autoscaler scales by an amount proportional to the predicted metric behavior. The prediction model estimates the magnitude of future demand and triggers proportional scaling actions, avoiding excessive resource allocation while ensuring sufficient capacity for predicted peaks.
Data Source
AI summary
Methods, systems, apparatuses, and computer-readable storage mediums described herein are configured to automatically allocate or deallocate computing resources based on a prediction of performance metrics behavior. For instance, the historical behavior of compute metrics (or a time series obtained therefor) is analyzed to detect a seasonality (i.e., a seasonal pattern) and a trend therefor. A prediction of the metrics' behavior for a future time frame is determined based on the seasonality and the trend. Based on the prediction, computing resources are allocated or deallocated at or prior to the future time frame occurring. For example, if a prediction is made that a particular metric will increase, additional compute resources are allocated to handle the increase ahead of the predicted metric increase. If a prediction is made that a particular metric will decrease, compute resources are deallocated at the time the metric is predicted to decrease.