In the rapidly evolving world of cloud computing, managing the scalability of resources in response to service demand has proven to be a key challenge. To meet this challenge, we developed Smart Scaler, a tool designed to automate the scaling of infrastructure and application resources. By predicting demand for services in advance, Smart Scaler ensures that resources are precisely matched to needs, optimizing both performance and costs.
The idea for Smart Scaler came from my experience with running SaaS platforms. Over the years I have worked closely with various teams to ensure that the system functions properly. I also led the Cloud Ops team whose primary responsibility was to ensure that systems consistently met service-level targets while aligning cloud service costs with cost of sales. However, this has been a significant point of friction in current operating models.
Operations teams struggle to balance service assurance with cost control, impeding the team’s ability to maintain the speed of development required for the business.
Teams must rapidly deploy different versions of microservices to maintain the momentum of developing new or improved features. The complexity of managing interactions between services from different teams adds another level of difficulty. Although most implementations follow a structured pipeline to production, from development to pre-production, performance engineering and finally to production, replicating performance standards in the real world remains a daunting task. Companies need to be able to maintain performance standards that reflect real-world scenarios, especially with the introduction of new or improved APIs. This puts enormous pressure on the performance engineering team to continuously update production data and ensure that the performance suite remains robust.
Scaling is a complex balancing act
Finding the balance between cost efficiency and service reliability is a significant challenge for site reliability engineering (SRE) teams. Corporations are often rewarded for providing superior services to end users, while corporate executives prioritize improving the profit margins of those services through cost-saving strategies. It is crucial for companies to find a balance between these two goals.
Employee well-being is also a key indicator of team health and productivity, highlighting the importance of achieving balance not only for optimal service performance, but also for fostering a productive and healthy work environment. As a result, scaling resources can be a difficult balancing act that requires balancing three outcomes: users must receive high-quality services, financial losses should be reduced, and employees must have a productive and healthy work environment. Automating the scaling process can help ensure that companies are deploying the correct amount of resources while limiting the need for employees to micromanage the scaling aspect of the deployment process.
Technical challenges in scaling
When determining how best to scale an application, I often wonder if the behavior of the application can be fully modeled by infrastructure metrics such as CPU and memory. It’s an often overlooked question, but it’s increasingly relevant in today’s diverse development environments, where teams choose appropriate programming languages based on their specific business needs. Each programming language has different needs in terms of memory requirements, so relying only on infrastructure metrics such as CPU or memory to model the behavior of complex applications is often inadequate.
The Kubernetes Horizontal Pod Autoscaler (HPA) attempts to mitigate scaling challenges by allowing the inclusion of custom metrics via API calls. However, it overlooks key aspects such as the temporal dynamics of these metrics and the intricate web of service dependencies. This one-size-fits-all approach to metrics, treating each as isolated to a specific service, does not take into account the interconnectedness of services. In addition, HPA’s reliance on infrastructure metrics does not provide a complete picture, as it does not account for the nuanced behaviors of applications housed within modules, including programming languages and specific application behavior.
The diversity in application statistics across implementations adds another layer of scalability complexity. While solutions such as Isto attempt to standardize metrics, these efforts are not yet integrated into scaling decisions. In addition, acceptance of the Istio is not universal, in part due to the operational hurdles presented by its sidecar setup model and difficulty in handling. Critical information about service failures is often buried in application logs, which cannot be easily modeled within a scaling solution.
Application context is critical to scaling pods for service assurance. Metrics such as queue depth, service-to-service latency, API error rate, and requests per second (RPS) on APIs, which serve as the backbone of microservices, should be factored into scaling decisions. In a microservices environment, understanding service chains and traffic proportionality is fundamental to effective scaling strategies.
There are also projects like Keda that aim to let developers define what factors trigger scaling, but teams still have to manually set scaling trigger points. This approach still misses the understanding of the end-to-end service chain, showing the clear limitations of infrastructure metrics in ensuring service reliability.
How Smart Scaler improves the scaling process
Smart Scaler uses advanced machine learning and reinforcement learning techniques to automate the scaling process, making it efficient and cost-effective. Machine learning excels in its ability to analyze application behavior, including key metrics such as API request rates per second, API error rates, service-to-service latency, service chain graphs, and CPU and memory usage. Machine learning can effectively process this vast amount of data and extract meaningful insights from it.
Unlike traditional analytical methods, machine learning models excel at analyzing disparate data sets and synthesizing outcomes that are tailored to specific goals. They also automate such tasks with different data sets which will lead to streamlining of day-to-day operations for individuals and organizations.
Smart Scaler also includes reinforcement learning, which offers a dynamic approach to data analysis without the need for constant retraining. Combining predictive modeling with reinforcement learning makes it even more powerful. Predictive modeling helps estimate future or unseen data based on patterns learned from the environment. This predictive approach is key to mitigating the challenges Kubernetes environments face when cluster capacity is at the edge and more nodes need to be added to the cluster during traffic spikes.
Smart Scaler can also help define rules about what type of node to bring into the cluster based on the application’s deployment manifest, scaling needs, and external policies like cost-saving initiatives. Predictive models help deploy infrastructure before there is a need to service spikes in traffic. While ramping up is critical during traffic bursts, ramping up is also critical for cost containment.
Conclusion
Avesha’s smart scale integrates reinforcement learning and predictive modeling to dynamically adjust HPA parameters. This ensures optimal performance while reducing costs, improving decision-making and improving resource utilization. With Smart Scaler providing predictive scaling for a set of services and considering service chains, deployments are now in a position to automate their scaling process, enabling more efficient and cost-effective management of cloud-native infrastructure.