Scaling success: Navigating the challenges of autoscaled applications with Site24x7 APM Insight

Have you ever found yourself wishing for a magical solution to handle the unpredictable ebb and flow of user traffic on your cloud-hosted platforms? Organizations today face the ever-present challenge of effectively managing fluctuating levels of traffic on their platforms. Enter application autoscaling, a concept in modern resource management that allows organizations to seamlessly adjust their resources in response to spikes or lulls in user activity. But what exactly is autoscaling? Simply put, autoscaling is the automated process of dynamically allocating resources such as servers, storage, or containers on demand and in real time. For better understanding, imagine you have a service that is ready to roll. Once your product goes live and grows, the demand for it may waver. In such cases, the resources need to be adjusted as you go. This is where autoscaling comes in. By harnessing the power of automation, autoscaling ensures that your workload's resources adapt to match the fluctuating demand.

Autoscaling comes with a plethora of advantages, with one of the most prominent being its ability to ensure that your SLAs are always met by managing resource allocation based on server load. By preemptively preparing for surges in traffic, autoscaling proactively manages heightened demand, thus diminishing the likelihood of application failures and streamlining cost and resource management. This proactive approach not only boosts the reliability and performance of your application but also enables organizations to address user needs while optimizing resource utilization efficiently.

Although autoscaling boasts an impressive list of benefits, it can also pose some challenges in a multi-cloud or hybrid cloud environment. In this blog, we'll delve deeper into some of the challenges of monitoring autoscaled applications and how Site24x7's application performance monitoring solution efficiently handles them.

Autoscaling introduces significant changes to an application's underlying infrastructure, making it challenging to monitor the application and maintain overall observability efficiently. A robust monitoring solution should be able to monitor autoscaled instances without hiccups while maintaining end-to-end observability at all times.

Let us look at an example of an e-commerce application to understand this better. It is natural for the application to experience seasonal fluctuations in traffic. Let us assume that the application has experienced an unexpected spike in traffic during its Black Friday promotions. When this happens, the application needs to be scaled up immediately to handle this sudden inflow of traffic before it crashes, which would cause customers to face downtime. Autoscaling handles this situation by facilitating the addition of new instances and allocating resources based on demand to manage the server load efficiently. However, when things return to normal, there will be no use for these newly created instances, and most cloud vendors will promptly remove them.

However, suppose your operations teams attempt to attain the observability of these instances using traditional monitoring systems. In doing so, they will receive alerts for each instance that goes offline, leading to alert fatigue. Moreover, depending on the monitoring tool employed, an additional license may be unnecessarily consumed for each instance that went offline, resulting in newly created instances being left as unmonitored blind spots within your application, disrupting the observable nature of the system.

One way to handle this issue is to mimic the same autoscaled behavior in your observability tool while retaining the historical data. Ideally, the monitoring tool should intuitively identify that your application is hosted in an autoscaled environment and automatically reassign unused instance slots, thereby making room for new instance data. In Site24x7's application performance monitoring solution, this occurs when the instance does not communicate continuously for at least five minutes, and it is reactivated when the application is scaled up again.

Some solutions can be employed to ensure that your application runs glitch-free. Golden signals are crucial for effectively monitoring autoscaled applications. These key metrics, which include latency, traffic, errors, and saturation, provide valuable insights into application health and performance as they adapt to changing demands. By closely watching these signals, operations teams can understand how applications behave across different instances and infrastructure configurations. This deep understanding allows proactive performance management, faster issue identification and resolution, and optimized resource allocation, ensuring smooth operation even during rapid scaling.

Autoscaling emerges as a game-changer in managing the unpredictable nature of user traffic on cloud-hosted platforms. However, while it offers numerous benefits, it also presents challenges, particularly in multi-cloud or hybrid cloud environments. To effectively navigate these challenges and ensure seamless operations, a robust monitoring solution is essential.