Serverless Auto-Scaling with Workload Prediction and Replica Pre-Warming
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Solution Overview
Problem
Current serverless computing platforms face performance degradation and inefficiency due to simplistic threshold-based auto-scaling approaches, which lead to wasted resources and inability to meet quality of service (QoS) requirements, especially under interactive and bursty workloads, and are plagued by cold start latency issues.
Innovation Solution
A proactive auto-scaling approach using workload prediction and control-theoretic resource orchestration, leveraging machine learning to predict future workloads and adjust resources in advance, with the option to directly manage replicas in private clouds or trigger auto-scaling in public clouds through fake requests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If threshold-based auto-scaling is used, then resource allocation is simplified, but performance degradation occurs and QoS requirements cannot be met
Solution Approach 1:
The system performs preliminary workload prediction using machine learning models to forecast future resource demands before they occur. This allows the auto-scaling system to proactively provision resources in advance, ensuring QoS requirements are met without waiting for threshold-based triggers, thereby resolving the contradiction between simple resource allocation and reliable QoS compliance.
2Reliability
If pre-purchased capacity is used, then performance is guaranteed, but cost efficiency decreases
Solution Approach 1:
The system dynamically adjusts resource allocation based on real-time workload conditions and predictive analytics. Instead of static pre-purchased capacity, the system continuously optimizes resource provisioning to match actual demand, ensuring performance guarantees are met only when necessary while minimizing resource waste during lower-demand periods, thus resolving the contradiction between reliability and resource efficiency.
3Ease of operation
If container orchestration is simplified, then ease of operation improves, but start-up overhead increases
Solution Approach 1:
The system maintains simplified container orchestration while reducing cold start latency through predictive pre-warming. Machine learning models forecast upcoming workload spikes and trigger early container initialization before demand occurs, allowing the system to keep orchestration simple while proactively minimizing start-up delays, thus resolving the contradiction between operational simplicity and start-up performance.
4Speed
If reactive auto-scaling is used, then response time is reduced, but resource allocation lags behind workload demands
Solution Approach 1:
The system combines reactive auto-scaling with proactive workload prediction to eliminate the lag between workload demands and resource allocation. By forecasting future resource needs using machine learning models, the system pre-provisions resources before actual demand spikes occur, maintaining fast response times while eliminating the inherent delay of purely reactive approaches, thus resolving the contradiction between response speed and allocation timeliness.
Data Source
AI summary
Example implementations relate to a proactive auto-scaling approach. According to an example, a target performance metric for an application running in a serverless framework of a private cloud is received. A machine-learning prediction model is trained to forecast future serverless workloads during a window of time for the application based on historical serverless workload information. The serverless framework is monitored to obtain serverless workload observations for the application. A future serverless workload for the application at a future time is predicted by the trained machine learning prediction model based on workload observations. A feedback control system is then used to output a new number of replicas based on a current value of the performance metric, the target performance metric and the predicted future serverless workload. Finally, the serverless framework is caused to scale and pre-warm a number of replicas supporting the application to the new number.