Best practices for cloud-based network monitoring

When cloud adoption grew rapidly in the early 2010s, businesses started facing new challenges. Managing distributed systems, monitoring cloud-hosted applications, and ensuring network performance across global infrastructures became more complex. This shift in how businesses run IT operations creates a clear need for cloud-based network monitoring tools that can give you real-time insights into performance, security, and overall system health.
Traditional network monitoring methods—built for static, on-premises environments—often struggle to keep up with the dynamic nature of cloud-based systems. With cloud-native architectures that constantly change—think containers, serverless functions, and auto-scaling resources—you need a more agile and scalable approach to monitoring.
In this article, you’ll explore best practices for cloud-based network monitoring, including tips for effective monitoring. From using centralized observability tools to adopting proactive Artificial Intelligence for IT Operations (AIOps) solutions, you’ll learn how to keep your cloud infrastructure secure and resilient as it grows.
Cloud-based network monitoring offers distinct advantages over traditional approaches, primarily due to its ability to provide continuous visibility into dynamic, distributed cloud environments. To effectively monitor cloud networks and their resources, consider the following best practices:
A unified, centralized observability platform is essential for gaining a comprehensive view of your cloud infrastructure. Cloud-native environments often involve multiple containers, serverless functions, microservices, and cloud providers; for example, Amazon Web Services (AWS), Azure, and Google Cloud. This means built-in monitoring tools won’t be enough. Without a centralized tool to bring all the data together, visibility can become fragmented, making it difficult to diagnose issues or optimize performance.
Adopting a single pane of glass solution allows you to consolidate monitoring data from various cloud providers, containers, and on-premises systems into a single interface. This approach simplifies the monitoring process, reduces complexity, and helps observability teams quickly diagnose and troubleshoot problems.
Pro tip: If you’re operating in a multi-cloud environment, make sure your monitoring tool integrates with various cloud-native monitoring solutions (eg AWS CloudWatch, Azure Monitor, Google Cloud Operations Suite) to gain a unified view across all clouds. LogicMonitor’s LM Envision platform is one example of a centralized observability platform that can augment your observability strategy by integrating smoothly with on-premises and multi-cloud environments, allowing you to unify all of your infrastructure monitoring under a single pane of glass.
The importance of monitoring the right metrics cannot be overstated. In a cloud environment—especially one that leverages microservices, containers, and serverless functions—traditional network performance metrics like packet loss and bandwidth utilization often don’t tell the whole story. Cloud-native applications have specific key performance indicators (KPIs) that are better suited to reflect the performance of services in real-time.
Some of the most important metrics to monitor in cloud environments include the following:
Pro tip: It’s beneficial to align your KPIs and monitoring approach with your business goals. For example, if your cloud application directly supports customer-facing services, make latency and uptime your top priorities. These metrics are key to ensuring a smooth user experience. Similarly, if you’re working with a microservices architecture, keeping a close eye on latency and failure rates is essential. It’s all about monitoring what truly matters to your business and users. It’s recommended to use powerful container monitoring solutions like the LogicMonitor Envision platform as it provides scalable, dynamic visibility into Kubernetes and Docker applications.
Cloud environments require fast, scalable responses to performance changes, scaling needs, and security threats. Manual intervention can slow down resolution times and introduce human error. Automation, in contrast, speeds up responses and reduces the likelihood of mistakes.
You can automate several critical tasks, such as the following:
A great way to achieve this automation is by leveraging the workflow integrations of observability platforms with automation tools like Ansible or Terraform. This enables you to create runbooks that automate tedious tasks, reduce manual intervention, improve consistency, and accelerate response times. Automation can also be purpose-built into an observability platform. For example, LM Envision features agentless collectors that automatically discover new resources, speeding up the process of onboarding new devices, and its event correlation solution, Edwin AI, automatically clusters multiple alerts into a single incident ticket in ServiceNow with a plain English summary of the issue and recommended remediation steps.
Pro tip: When setting up automation tools, be sure to test and refine your processes regularly to ensure that they remain effective and efficient as changes to the infrastructure or application could otherwise lead to inconsistencies. If automation is not kept up-to-date, it will become outdated and ineffective. Properly maintained and adapted automation can greatly enhance your organization’s agility and operational efficiency.
In cloud environments, real-time monitoring is crucial for maintaining service level agreements (SLAs) and ensuring uptime and performance. Delays in detecting issues can result in downtime, poor user experiences, or even security vulnerabilities.
Set up proactive alerts for important metrics, like the following:
Modern tools like the LM Envision platform can integrate with incident response platforms, such as ServiceNow or PagerDuty to automate your response to critical issues. This enables faster incident management and improves your team’s ability to resolve issues before they impact users.
One of the biggest advantages of cloud-based monitoring is the ability to scale in tandem with your infrastructure. As your cloud resources grow, your monitoring solution should be able to scale accordingly without a degradation in performance.
Make sure that your observability platform is cloud-native and designed for scalability. It should be able to handle large volumes of data without impacting the performance of your network or applications. Additionally, your monitoring tools should have high availability to ensure that they remain operational even during system failures or infrastructure scaling events.
While traditional network monitoring focuses on reactive responses to issues, predictive monitoring allows you to identify potential problems before they impact performance. It’s not a good idea to wait for incidents to happen—anticipating and addressing issues proactively is the better approach. This is where next-gen AIOps tools can help.
Analyzing historical data and applying machine learning algorithms allow predictive monitoring tools to detect patterns and forecast potential issues—such as traffic spikes, system failures, or resource exhaustion—before they happen.
Pro tip: If you’re considering implementing predictive monitoring, LogicMonitor Edwin AI (a GenAI assistant for IT observability) is a great tool. It uses observability data and unstructured knowledge from various tools, rather than simply being a ChatGPT-wrapper solution. It also works seamlessly across multiple platforms, regardless of the underlying infrastructure.
From implementing centralized observability tools to automating critical tasks and adopting predictive monitoring, these best practices are designed to help organizations maintain optimal performance to keep pace with the dynamic nature of cloud environments. With traditional monitoring methods often struggling to keep up with ephemeral cloud resources like containers and serverless functions, adopting the right tools and techniques is key to achieving comprehensive, real-time visibility.
If you’re looking for a powerful tool that provides end-to-end observability across your cloud and hybrid infrastructures, consider LM Envision. It integrates seamlessly with leading cloud providers, giving you a unified view of your network performance, resource utilization, and security posture. Whether you’re managing a multi-cloud environment, automating incident response, or implementing predictive monitoring, LogicMonitor equips you with the insights needed to manage your cloud-based network resources and scale with confidence proactively.
Blogs
See only what you need, right when you need it. Immediate actionable alerts with our dynamic topology and out-of-the-box AIOps capabilities.