A completely new way for K8s Autoscaling: Why Predictive Pod Scaling with Smart Scaler and Karpenter is needed before plain VPA

🛠️🛠️ K8s Autoscaling_ (1).jpg
Raj Nair
Raj Nair

Founder & CEO

9 May, 2024

5 min read



In cloud-native architectures, efficient autoscaling mechanisms are indispensable for optimizing resource utilization and ensuring optimal performance. Traditionally, autoscaling solutions have relied on reactive approaches, adjusting resource allocation after-the-fact based on current  or past demand.

However, the advent of predictive pod scaling, powered by Smart Scaler and Karpenter, represents a paradigm shift towards proactive resource management that revolutionizes the way we scale applications.

Let's look at why predictive pod scaling with Smart Scaler and Karpenter outshines the traditional approach using Vertical Pod Autoscaler (VPA) with Kubernetes-based Event-Driven Autoscaling (KEDA), all of which are reactive in nature.

 Maximizing Resource Utilization

 One of the primary advantages of predictive pod scaling is its ability to fill pods to their capacity “even before” triggering autoscaling actions. What we mean by that is historical data is analyzed and predictive traffic models are leveraged, thus enabling Smart Scaler to anticipate demand surges and proactively adjust resource allocation. This ensures that resources are utilized optimally, minimizing both under-provisioning and over-provisioning scenarios.

 Horizontal and Vertical Scaling Flexibility

 Predictive pod scaling offers great flexibility in scaling strategies, allowing for both horizontal and vertical scaling based on workload characteristics. With Smart Scaler, organizations can easily transition between horizontal scaling to add more instances of a service and vertical scaling to adjust the resource allocation of existing instances. This scaling approach ensures that applications can efficiently handle fluctuating workloads while minimizing resource wastage.

Predictive Traffic Modeling

 The cornerstone of predictive pod scaling is predictive traffic modeling, which enables organizations to anticipate demand fluctuations with high accuracy. Historical traffic patterns and trends are analyzed, and with those insights Smart Scaler predicts future workload requirements, allowing for proactive scaling actions. This proactive approach not only improves resource utilization but also enhances application responsiveness by preemptively allocating resources to meet anticipated demand.

Rapid Response to Spikes

 In scenarios where unexpected traffic spikes occur, predictive pod scaling with Smart Scaler ensures rapid response and recovery. Leveraging its neural network that is trained in handling various types of spikes, Smart Scaler can swiftly adjust resource allocation to accommodate sudden increases in demand. This agility in scaling ensures minimal disruption to application performance and user experience, thereby enhancing overall reliability and resilience.

Event-Based Scaling

 The integration of an event scaler further enhances the versatility of predictive pod scaling. By allowing scaling actions to be pre-arranged using a calendar-based approach, organizations can proactively prepare for anticipated events such as product launches or marketing campaigns. This proactive planning ensures that applications are adequately scaled to handle anticipated workload spikes, minimizing the risk of performance degradation during critical periods.

 Enhanced Performance and Stability

 Perhaps one of the most surprising outcomes of predictive pod scaling is its ability to improve application response times and stability.


See figure above showing real-world data where the portion to the left of the shaded portion is the result of using plain HPA and the shaded portion exemplifies what happens when Smart Scaling is used. Note in the upper panel, the orange line is the number of pods that is held to a higher artificial minimum by HPA. The green line is the traffic load. The lower panel shows that response time considerably reduces and becomes very controlled even with greater traffic spikes -- potentially resulting in better APDEX scores. Smart Scaler takes into account inter-service dependencies and avoids the creation of internal bottlenecks. With this approach, Smart Scaler ensures that resources are allocated optimally to maintain a steady communication and data flow between microservices. This holistic approach to resource management not only enhances performance but also promotes stability and reliability across the entire application ecosystem.

In contrast to plain VPA, the philosophy behind Smart Scaler is to maximize the available capacity and reach high utilization without any artificial utilization limiting scaling threshold like HPA uses. Thus, you scale pods and leave the node packing and scaling to Karpenter. Finally, if it is the case that the traffic is much lower than even a single pod’s capacity, that’s when you need to use VPA to reduce the pod’s excess capacity.

In conclusion, predictive pod scaling with Smart Scaler and Karpenter represents a significant advancement in autoscaling technology, offering unparalleled flexibility, efficiency, and reliability. Using entities such as predictive analytics, event-based scaling, and proactive resource management, organizations can optimize resource utilization, enhance application performance, and ensure seamless scalability in the face of evolving workload demands. As cloud-native architectures continue to evolve, there is plenty of innovation to be brought about in predictive pod scaling to empower organizations to achieve greater agility, efficiency, and resilience in their digital transformation journey.