Understanding Autoscaling

Before we discuss observability, let’s briefly explore the concept of autoscaling. Autoscaling refers to the ability of an application or infrastructure to automatically adjust its resources based on demand. It enables organizations to handle fluctuating workloads and optimize resource allocation efficiently.

Observability, in the context of autoscaling, refers to gaining insights into an autoscaling system’s performance, health, and efficiency. It involves collecting, analyzing, and visualizing relevant data to understand the application and infrastructure’s behavior and patterns. Through observability, organizations can make informed decisions to optimize autoscaling algorithms, resource allocation, and overall system performance.

Critical Components of Autoscaling Observability

To achieve effective autoscaling observability, several critical components come into play. These include:

Metrics and Monitoring: Gathering and monitoring key metrics such as CPU utilization, response times, request rates, and error rates is fundamental for understanding the application and infrastructure’s performance.

Logging and Tracing: Logging captures detailed information about events and transactions within the system, while tracing provides insights into the flow of requests across various components. Both logging and tracing contribute to a comprehensive understanding of system behavior.

Alerting and Thresholds: Setting up appropriate alerts and thresholds based on predefined criteria ensures timely notifications when specific conditions are met.  

Product: Cisco AppDynamics

### Real-Time Monitoring and Visibility

One of the standout features of Cisco AppDynamics is its ability to provide real-time monitoring and visibility into your application’s performance. By tracking key metrics such as response times, throughput, and error rates, AppDynamics allows you to quickly identify and resolve performance bottlenecks. This level of visibility ensures that you can maintain optimal application performance and deliver a superior user experience.

### Advanced Analytics and Insights

Cisco AppDynamics goes beyond basic monitoring by offering advanced analytics and insights. With its powerful analytics engine, you can gain a deep understanding of your application’s behavior and performance trends. This enables you to make data-driven decisions, optimize resource allocation, and improve overall efficiency. The platform’s machine learning capabilities also help in predicting potential issues before they impact your users.

### End-User Experience Monitoring

Understanding the end-user experience is crucial for the success of any application. Cisco AppDynamics provides comprehensive end-user experience monitoring by tracking user interactions and performance across different devices and geographies. This helps you identify and address issues that may affect user satisfaction. With detailed user journey maps, you can pinpoint areas that need improvement and ensure a seamless experience for your users.

### Business Transaction Monitoring

Cisco AppDynamics offers a unique approach to monitoring by focusing on business transactions. Instead of just monitoring infrastructure components, AppDynamics tracks entire business transactions from start to finish. This holistic view allows you to understand the impact of application performance on your business outcomes. By correlating performance data with business metrics, you can prioritize efforts that drive business value.

### Integration and Scalability

Cisco AppDynamics is designed to integrate seamlessly with your existing IT ecosystem. It supports a wide range of technologies and platforms, making it easy to deploy and scale as your business grows. Whether you are running applications on-premises, in the cloud, or in a hybrid environment, AppDynamics provides the flexibility and scalability you need to manage your application performance effectively.

Tools and Technologies for Autoscaling Observability

A wide range of tools and technologies are available to facilitate autoscaling observability. Prominent examples include Prometheus, Grafana, Elasticsearch, Kibana, and CloudWatch. These tools provide robust monitoring, visualization, and analysis capabilities, enabling organizations to gain deep insights into their autoscaling systems.

The first component of observability is the channels that convey observations to the observer. There are three channels: logs, traces, and metrics. These channels are common to all areas of observability, including data observability.

• Logs

Logs are the most typical channel and take several forms (e.g., a line of free text or JSON). They are intended to encapsulate information about an event.

• Traces

Traces allow you to do what logs don’t—reconnect the dots of a process. Because traces represent the link between all events of the same process, they allow the whole context to be derived from logs efficiently. Each pair of events, an operation, is a span that can be distributed across multiple servers.

• Metrics

Finally, we have metrics. Every system state has some component that can be represented with numbers, and these numbers change as the state changes. Metrics provide a basis of information that allows an observer to understand using factual information and leverage mathematical methods to derive insight from even a large number of metrics (e.g., the CPU load, the number of open files, the average number of rows, the minimum date).

Understanding VPC Flow Logs

VPC Flow Logs capture information about the IP traffic going in and out of Virtual Private Clouds (VPCs) within Google Cloud. Enabling VPC Flow Logs allows you to gain visibility into network traffic at the subnet level, thereby facilitating network troubleshooting, security analysis, and performance monitoring.

Once the VPC Flow Logs are enabled and data starts flowing in, it’s time to tap into the potential of Google Cloud Logging. Using the appropriate filters and queries, you can sift through the vast amount of log data and extract meaningful insights. Whether it’s identifying suspicious traffic patterns, monitoring network performance metrics, or investigating security incidents, Google Cloud Logging provides a robust set of tools to facilitate these analyses.

Auto Scaling Observability

Metrics: Resource Utilization Only

Metrics help us understand resource utilization. In a Kubernetes environment, these metrics are used for auto-healing and auto-scheduling. Monitoring performs several functions when it comes to metrics. First, it can collect, aggregate, and analyze metrics to identify known patterns that indicate troubling trends.

The critical point here is that it shifts through known patterns. Then, based on a known event, metrics trigger alerts that notify when further investigation is needed. Finally, we have dashboards that display the metrics data trends adapted for visual consumption.

These monitoring systems work well for identifying previously encountered known failures but don’t help as much for the unknown. Unknown failures are the norm today with disgruntled systems and complex system interactions.

Metrics are suitable for dashboards, but there won’t be a predefined dashboard for unknowns, as it can’t track something it does not know about. Using metrics and dashboards like this is a reactive approach, yet it’s widely accepted as the norm. Monitoring is a reactive approach best suited for detecting known problems and previously identified patterns. 

Metrics and intermittent problems?

The metrics can help you determine whether a microservice is healthy or unhealthy within a microservices environment. Still, a metric will have difficulty telling you if a microservices function takes a long time to complete or if there is an intermittent problem with an upstream or downstream dependency. So, we need different tools to gather this type of information.

We have an issue with auto-scaling metrics because they only look at individual microservices with a given set of attributes. So, they don’t give you a holistic view of the problem. For example, the application stack now exists in numerous locations and location types; we need a holistic viewpoint.

A metric does not give this. For example, metrics track simplistic system states that indicate a service is running poorly or may be a leading indicator or an early warning signal. However, while those measures are easy to collect, they don’t turn out to be proper measures for triggering alerts.

Example: Latency & Cloud Trace

Latency, in the context of applications, refers to the time it takes for a request to travel from the user to the server and back. Various factors, such as network delays, server processing time, and database queries influence it. Understanding latency is essential for developers to identify bottlenecks and optimize their applications for better performance.

Google Cloud Trace is a powerful tool provided by Google Cloud Platform that allows developers to analyze and diagnose application latency issues. By integrating Cloud Trace into their applications, developers can gain valuable insights into their code’s performance and identify areas for improvement.

Developers need to capture traces to analyze application latency effectively. Traces provide a detailed record of a request’s execution path, allowing developers to pinpoint the exact areas where latency occurs. With Cloud Trace, developers can easily capture and visualize traces in a user-friendly interface.

Auto-scaling metrics: Issues with dashboards:

Useful only for a few metrics

So, these metrics are gathered and stored in time-series databases, and we have several dashboards to display these metrics. These dashboards were first built, and there weren’t many system metrics to worry about. You could have gotten away with 20 or so dashboards. But that was about it. As a result, it was easy to see the critical data anyone should know about for any given service. Moreover, those systems were simple and did not have many moving parts. This contrasts with modern services that typically collect so many metrics that fitting them into the same dashboard is impossible.

Auto-scaling metrics:

Issues with aggregate metrics

So, we must find ways to fit all the metrics into a few dashboards. Here, the metrics are often pre-aggregated and averaged. However, the issue is that the aggregate values no longer provide meaningful visibility, even when we have filters and drill-downs. Therefore, we need to predeclare conditions that describe conditions we expect in the future. 

This is where we use instinctual practices based on past experiences and rely on gut feeling. Remember the network and software hero? It would help to avoid aggregation and averaging within the metrics store. On the other hand, we have percentiles that offer a richer view. Keep in mind, however, that they require raw data.

Auto Scaling Observability: Any Question

For auto-scaling observability, we take on an entirely different approach. They strive for other exploratory methods to find problems. Essentially, those operating observability systems don’t sit back and wait for an alert or something to happen. Instead, they are always actively looking and asking random questions to the observability system.

Observability tools should gather rich telemetry for every possible event, having full content of every request and then being able to store it and query it. In addition, these new auto-scaling observability tools are specifically designed to query against high-cardinality data. High cardinality allows you to interrogate your event data in any arbitrary way that we see fit. Now, we ask any questions about your system and inspect its corresponding state. 

Key Auto Scaling Observability Considerations

No predictions in advance.

Due to the nature of modern software systems, you want to understand any inner state and services without anticipating or predicting them in advance. For this, we need to gain valuable telemetry and use some new tools and technological capabilities to gather and interrogate this data once it has been collected. Telemetry needs to be constantly gathered in flexible ways to debug issues without predicting how failures may occur. 

Conditions affecting infrastructure health change infrequently and are relatively more straightforward to monitor. In addition, we have several well-established practices to predict, such as capacity planning and the ability to remediate automatically, e.g., auto-scaling in a Kubernetes environment. All of these can be used to tackle these types of known issues.

Due to its relatively predictable and slowly changing nature, the aggregated metrics approach monitors and alerts perfectly for infrastructure problems. So here, a metric-based system works well. Metrics-based systems and their associated signals help you see when capacity limits or known error conditions of underlying systems are being reached.

So, metrics-based systems work well for infrastructure problems that don’t change much but fall dramatically short in complex distributed systems. For these systems, you should opt for an observability and controllability platform. 

Closing points: Understanding Autoscaling

Autoscaling is a mechanism that automatically adjusts the number of computing resources allocated to an application based on its demand. By dynamically scaling resources up or down, autoscaling enables organizations to handle fluctuating workloads efficiently. However, robust observability is crucial to harness the power of autoscaling truly.

The Role of Observability in Autoscaling

Observability is the ability to gain insights into a system’s internal state based on its external outputs. It plays a pivotal role in understanding the system’s behavior, identifying bottlenecks, and making informed scaling decisions regarding autoscaling. It provides visibility into key metrics like CPU utilization, memory usage, and network traffic. With observability, you can make data-driven decisions and ensure optimal resource allocation.

 Monitoring and Metrics

To achieve effective autoscaling observability, comprehensive monitoring is essential. Monitoring tools collect various metrics, such as response times, error rates, and resource utilization, to provide a holistic view of your infrastructure. These metrics can be analyzed to identify patterns, detect anomalies, and trigger autoscaling actions when necessary. You can proactively address performance issues and optimize resource utilization by monitoring and analyzing metrics.

Logging and Tracing

In addition to monitoring, logging, and tracing are critical components of autoscaling observability. Logging captures detailed information about system events, errors, and activities, enabling you to troubleshoot issues and gain insights into system behavior. Tracing helps you understand the flow of requests across different services. Logging and tracing provide a granular view of your application’s performance, aiding in autoscaling decisions and ensuring smooth operation.

Automation and Alerting

Automation and alerting mechanisms are vital to mastering autoscaling observability. You can configure thresholds and triggers that initiate autoscaling actions based on predefined conditions by setting up automated processes. This allows for proactive scaling, ensuring your system is constantly optimized for performance. Additionally, timely alerts can notify you of critical events or anomalies, enabling you to take immediate action and maintain the desired scalability.

Autoscaling observability is the key to unlocking its true potential. By understanding your system’s behavior through comprehensive monitoring, logging, and tracing, you can make informed decisions and ensure optimal resource allocation. With automation and alerting mechanisms, you can proactively respond to changing demands and maintain high efficiency. Embrace autoscaling observability and take your infrastructure management to new heights.