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.

Key takeaways

Cloud vs. traditional monitoring: Traditional network monitoring tools struggle with the dynamic, ephemeral nature of cloud resources like containers and serverless functions.

Cloud-based monitoring matters: It improves resource efficiency, simplifies management, and ensures better scalability by providing real-time insights, centralized control, and flexible scaling without the need for complex on-premises infrastructure.

Best practices to implement: Use centralized observability tools, measure the right metrics, automate critical tasks, implement real-time monitoring and alerting, ensure scalability and high availability, and adopt predictive monitoring.

Predictive monitoring adoption: Use AI-driven insights and automation through AIOps tools to predict issues and take corrective actions before they affect performance or availability, moving you from alert fatigue to AI-driven efficiency.

Best practices to implement for effective cloud-based network monitoring

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:

Use a centralized observability tool

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.

Measure the right metrics

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:

Network latency

• Why it matters: Network latency measures the delay in data transmission between different systems or services in your cloud environment. High latency can negatively impact user experience, especially for real-time applications.
• What to watch for: Keep an eye on spikes in latency, which can signal network issues or resource contention that could degrade performance.

Throughput (traffic volume)

• Why it matters: Throughput refers to the amount of data being transferred across your network, which helps assess your infrastructure’s ability to handle large traffic volumes.
• What to watch for: Sudden increases in throughput may require scaling or load balancing to prevent network congestion.

Error rates

• Why it matters: Error rates track the frequency of errors occurring within applications, APIs, or cloud services. A high error rate can point to bugs, issues with configuration, or other failures.
• What to watch for: A sudden spike in error rates should be investigated immediately as it can indicate serious performance or security problems.

Uptime and availability

• Why it matters: Uptime and availability metrics measure the reliability and accessibility of your cloud services or infrastructure. Cloud environments should ideally have high availability.
• What to watch for: Downtime or disruptions can lead to service outages, so continuous monitoring ensures that any issues are quickly identified and resolved.

Service level KPIs

• Why it matters: KPIs act as a dashboard, providing organizations with actionable data to drive informed decisions. Without proper monitoring, it’s difficult to gauge whether the cloud strategy is successful, identify areas for improvement, or understand if resources are being utilized efficiently.
• What to watch for: Monitor KPIs that measure application performance, cloud infrastructure, cloud visibility, operational efficiency, and cloud governance and automation. LogicMonitor’s Service Insight feature helps users focus on the health and performance of the overall service, regardless of changes in underlying resources, by providing a complete, long-term view of your application’s health and performance. Service Insight aggregates data across all of your services by surfacing key performance indicators across geographically dispersed and ephemeral resources. For operational efficiency and cloud governance and automation, other key KPIs to track include mean time to detect (MTTD), mean time to resolve (MTTR), incident volume, percent of policies in a compliant state, and time to deployment. These KPIs provide the insights you need to ensure your cloud infrastructure is fully aligned with your organization’s cloud strategy.

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.

Automate critical tasks

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:

• Scaling resources: Automatically scale infrastructure based on predefined metrics, like CPU usage or memory utilization, to meet demand spikes.
• Patching: Automate patching for cloud resources to ensure security vulnerabilities are addressed without delay.
• Deploying monitoring agents: Automatically deploy monitoring agents to newly created cloud instances or containers.
• Responses to common incidents: If there’s service degradation or a threshold breach, setting up automated triggers can instantly take corrective action without waiting for manual intervention.

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.

Implement real-time monitoring and alerting

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:

• Latency, downtime, or performance degradation
• Security breaches (unauthorized access or suspicious activity)
• Resource utilization (eg CPU or memory usage thresholds being breached)

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.

Ensure scalability and high availability

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.

Adopt predictive monitoring with AIOps

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.

Conclusion

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.