Best practices for defining a cloud monitoring strategy

What is cloud monitoring?

Cloud monitoring is the process of observing, tracking and managing cloud-based IT infrastructure, applications and services. It involves collecting and analyzing data from various cloud resources to ensure optimal performance, security and cost efficiency.

At its core, cloud monitoring provides visibility into the health, performance and use of cloud services. This includes tracking metrics, such as CPU utilization, memory usage, network traffic and application response times. By continuously gathering and analyzing this data, organizations can do the following:

1. Identify and resolve issues before they impact end users.
2. Optimize resource allocation and reduce costs.
3. Ensure compliance with security policies and industry regulations.
4. Make data-driven decisions about capacity planning and scaling.

Cloud monitoring differs from traditional on-premises monitoring in its scope and complexity. With cloud environments spanning multiple services, regions and even providers, a comprehensive monitoring strategy must account for their distributed nature, while providing a unified view of the entire ecosystem.

What are the different types of cloud monitoring?

Cloud monitoring is multifaceted, encompassing various aspects of the cloud infrastructure and applications. Here are the primary types of cloud monitoring that organizations should consider:

1. Website performance. This type focuses on tracking metrics such as page load times, UX and other important cloud KPIs. It helps administrators understand how sites are performing against expectations, how visitor counts or webpage elements impact browsing performance, and whether search engine optimizations are effective. Cloud service providers (CSPs) often offer tools to capture these data points for comparison against established KPIs.
2. Cloud storage. This involves measuring remote storage operations, observing storage volume layouts and providing insights into data organization. Effective storage monitoring can highlight inefficient capacity usage, reveal potential security vulnerabilities and help optimize data management processes.
3. Database. By tracking database requests, queries, data integrity and user activity, organizations can identify patterns that inform necessary changes and upgrades. This is particularly crucial for applications that rely heavily on database operations. Tools like Datadog offer comprehensive database monitoring capabilities.
4. VM. For organizations that are using IaaS, VM monitoring is essential. It involves tracking user activity, performance metrics and individual infrastructure components to ensure optimal resource utilization and performance. Microsoft Azure Monitor is an example of a tool that provides extensive VM monitoring features.
5. Virtual network. This encompasses oversight and protection of virtual network components, such as firewalls, switches, routers and software-based load balancers. Real-time network monitoring helps IT teams assess performance, identify bottlenecks and uncover potential security concerns. Amazon CloudWatch is a popular choice for network monitoring in AWS environments.
6. Application performance. Application performance monitoring (APM) tools provide insights into how applications behave on user devices, often through real-time dashboards that display user satisfaction metrics. This type of monitoring is crucial for maintaining a positive experience for the user and quickly identifying and resolving application issues. New Relic is a well-known APM system.
7. Security. With the increasing prevalence of cyberthreats, security monitoring has become a critical component of cloud strategies. It involves tracking user activities, monitoring for unusual patterns and ensuring compliance with security policies and regulations. Organizations should consider implementing comprehensive compliance monitoring strategies to address security concerns effectively.
8. Cost and billing. As cloud costs can quickly spiral out of control, monitoring resource consumption and associated costs is vital. This type of monitoring helps organizations optimize their cloud spending and maintain budget control. Developing a comprehensive cloud monitoring strategy that includes cost monitoring is essential for long-term success in the cloud.

By implementing these various types of cloud monitoring, organizations can ensure optimal performance, security and cost-effectiveness of their cloud infrastructure and applications.

Why is cloud monitoring important?

Cloud monitoring opens a window into your cloud services' functionality at any given moment. Knowing what takes place in a SaaS, PaaS, IaaS or cloud-hosting service empowers your teams. Keeping an eye on performance activity also can help providers and developers spot potential improvements that benefit end users, such as better resource allocation and load balancing. Cloud monitoring tools can also help position your services for scaled growth.

So, what can be monitored? The key indicators of ecosystem health are as follows:

• Performance -- throughput, latency, memory usage, response time, user capacity.
• Reliability -- uptime and downtime, average time between failures, time to repair, error handling.
• Security -- DDoS attack resistance, blast radii, access control, data protections.
• Costs and billing -- estimated and accrued charges for cloud resources the organization's workloads consume to help keep its cloud use and spending under control.

Keeping track of so many indicators might seem daunting. Monitoring tools enable you to pool application data into a centralized space, where the information is organized and discoverable by numerous stakeholders.

How does cloud monitoring work?

Cloud monitoring operates through a continuous cycle of data collection, analysis and action. Specialized software tools deployed across the cloud infrastructure collect real-time data from various sources, including applications, servers, networks and databases. This data typically includes metrics like CPU usage, memory utilization, network traffic and application response times.

Once collected, the data is processed and analyzed to identify patterns, anomalies and potential issues. Advanced analytics and machine learning (ML) algorithms often assist in the process, helping to detect subtle changes that might indicate emerging problems. The analyzed information is then presented in dashboards and reports, providing IT teams with a comprehensive view of the cloud environment's health and performance. When predefined thresholds are breached or anomalies detected, the monitoring system triggers alerts, notifying relevant personnel.

Many cloud monitoring solutions also offer automated responses to certain events, such as auto scaling resources during traffic spikes or initiating failover procedures during outages.

Which cloud services should be monitored?

Enterprises should prioritize monitoring these critical cloud services:

1. Compute services, e.g., Amazon EC2, Azure VMs. These are the workhorses of cloud applications. Monitoring is essential because performance issues here can cascade throughout the entire system. By tracking CPU usage, memory consumption and instance health, you can prevent systemwide slowdowns and unexpected downtime.
2. Database services, e.g., Amazon Relational Database Service, Azure SQL. Databases often become bottlenecks in cloud systems. Monitoring query performance, connection counts and storage usage helps maintain responsive applications and can prevent data loss or corruption.
3. Storage services, e.g., Amazon S3, Azure Blob Storage. These services often handle sensitive data and high traffic volumes. Monitoring is crucial for detecting unusual access patterns that could indicate security breaches and for managing costs as storage needs grow.
4. Network services, e.g., AWS VPC Flow Logs, Azure Network Watcher. Effective network monitoring can identify potential DDoS attacks, pinpoint the cause of latency issues and ensure that data is flowing efficiently between services and to end users.
5. Serverless functions, e.g., AWS Lambda, Azure Functions. While "serverless," these still require monitoring. Tracking execution times and error rates can uncover code inefficiencies, while monitoring invocations helps control costs that can quickly spiral if left unchecked.

Monitoring these core services provides a comprehensive view of your cloud ecosystem's health, performance and security, enabling proactive management and optimization.

How does cloud monitoring benefit an organization?

Consider a modern automobile. Multiple systems and mechanical parts work in tandem, and diagnostic work for such complex systems and parts is a big undertaking. An onboard diagnostic system stores trouble codes and tracks real-time engine performance. Engineers can tweak these systems with programming changes.

Similarly, cloud monitoring reveals where problems lurk, so IT professionals can step in and act before those problems affect wider parts of the system and affect users. For example, if an app consumes too much memory or compute resources, IT staff can adjust resource provisioning. Active monitoring is immensely helpful, though retrospective logging can illuminate worrisome trends.

Unlike traditional services that are monolithic or bundled under one large codebase, microservices have their own code, resources and programmable logic. Developers run their applications in isolated containers that generate their own data and claim their own resource allocations. It gets complicated, especially at scale. Tracking the metrics can help alleviate growing pains.

Keep in mind that a cloud monitoring strategy doesn't just uncover problems; it highlights what you're doing well so you only devote attention to things that need improvement.

Greater visibility and a data-driven approach can bring the following advantages:

• Better performance. Raw metrics and organized infographics can provide clearer pictures of a system's performance, especially in a containerized environment. Knowledge about resource use and allocation, as well as how application demand causes strain, can help teams optimize their deployments.
• Better security. User activity logs and role-based access control tactics help admins tighten unauthorized access. Teams can measure the impacts of traffic to understand the potential severity of a DDoS attack. They also can regularly scan files and resources to prevent malware or other afflictions from gaining a foothold.
• Topographical understanding. Observability and clear views into an infrastructure's unique layout help teams understand how components are arranged. This knowledge makes it much easier to navigate the ecosystem during management tasks.
• Better cohesiveness. Cloud monitoring tools often pool human-readable data or charts into a centralized location. While this data was once segregated between teams, new databases can provide business value to all teams with one data set.

Cloud monitoring best practices

There are many ways to tackle cloud monitoring, but some consensus recommendations and best practices apply across most cases:

1. Determine which metrics mean the most to your organization. What do you most want to accomplish through monitoring? Performance, security or reliability might take precedence over other areas. Many companies improve their services based on customer preferences. For example, multiplayer gaming services might favor low latency and high capacity at the expense of security.
2. Choose tooling based on core metrics. Sometimes, a business gets too far ahead of itself and shops for a monitoring tool before it settles on a strategy -- which metrics to prioritize, services to monitor and providers to use. Consider your budget and technology stacks. Teams that maintain Docker-based applications have different needs than ones that conduct e-commerce. However, tools can't be all things to all teams. Each one has strengths and weaknesses. Users might simply prefer one interface over another, everything else being equal.
3. Establish performance baselines. Numbers mean nothing without context. Create a performance baseline to understand if your system is acting irregularly or to spec. This gives you a point of comparison and normal operating range for your cloud services. Tools like Amazon CloudWatch and Azure Monitor can help establish these baselines.
4. Monitor UX. Users are everything, and services should exist to improve user outcomes. Enterprises often measure them with features, but UX typically relies on reducing friction, such as frustration from crashes, service interruptions, errors or bottlenecks. APM tools, like New Relic and Datadog, can show how well an application behaves on user devices through dashboards that paint a real-time picture of satisfaction.
5. Implement continuous monitoring and improvement. Use your monitoring tool to improve testing procedures. Failures will occur at some point. Cloud monitoring continuously enables chaos testing for high-traffic applications and web services. Regularly review and refine the monitoring strategy as you collect more data and gain insights.
6. Automate when possible. There's an adage in IT: If you perform a task more than once, automate it. Teams can offload to their monitoring tool key tasks such as event-based responses, configuration changes, periodic health checks and timed reports. Automate administrative duties wherever possible to save time for more important tasks.
7. Establish targeted alerting. Alerts that reach the right team members help immensely with issue remediation. Monitoring software can send messages via text, email or even via mediums such as Slack. Set thresholds that help maintain efficiency so the tool can trigger the right solution if activity goes above or below them.
8. Monitor cloud costs. The more you use your cloud monitoring service, the more it costs you. A strong cloud monitoring tool helps you keep track of all fees associated with usage and activity in your cloud architecture. Implement cloud-based cost intelligence software to see your cloud investment's what, why and how.
9. Centralize and consolidate monitoring data. It is essential to have a monitoring system that consolidates all your data gathered from different sources into one place. This provides much cleaner and organized use of metrics in a complete 360-degree performance review. Display the data in unified dashboards and charts to reduce the need for using multiple tools, services and APIs.
10. Emphasize cross-team collaboration. Collect insights from different teams on what data is important to them, how to best view it and what to do with it. This helps in mapping monitoring metrics to business outcomes within your organization.
11. Regularly test your monitoring strategy. Continuously test your cloud monitoring tools to ensure they are fully functional in the event of a breach. Through regular testing, you might uncover weak spots and vulnerabilities that might prompt you to adopt new standards for the alert system.

Remember, while comprehensive monitoring is crucial, it's equally important to maintain a good signal-to-noise ratio. Prioritize the metrics and events that affect your bottom line the most to avoid overwhelming your teams with excessive information. A well-implemented cloud monitoring strategy based on these best practices can significantly enhance your cloud operations, improving performance, security and cost efficiency.

Cloud monitoring tools and dashboards

Real-time monitoring is powerful but demanding on IT staff. Fortunately, CSPs offer AI- and ML-enhanced tools to automate and improve many monitoring capabilities. Azure, AWS and Google Cloud all provide AI-driven monitoring products focusing on resource use, cost optimization and network performance.

While some cloud monitoring tools offer comprehensive visibility, others may have limitations due to data access restrictions. AI-driven anomaly detection can help identify these gaps and suggest alternatives. Additionally, AI-powered predictive analytics can anticipate fluctuations in containerized environments and adjust monitoring frequency accordingly, addressing the challenges of intermittent data capture in dynamic cloud setups.

But it's not all AI hype. These tools provide practical, tangible benefits, such as real-time alerts, detailed performance metrics and customizable dashboards, that help IT teams quickly identify and resolve issues.

Examples of cloud monitoring tools

The best cloud monitoring tool should engage and inform without prioritizing superfluous data, employing AI to highlight what's truly important. It should align with your ecosystem's unique arrangement and the IT team's familiarity with the technology stack, as well as offer useful integrations. Here are some noteworthy picks for monitoring your cloud services in 2024, listed in alphabetical order:

• Amazon CloudWatch remains a crucial tool for AWS users, with continuous improvements in its AI and ML features. Its native compatibility with AWS features provides unhindered data capture and analysis for Amazon EC2 and other AWS resources. CloudWatch's anomaly detection uses ML models to continuously analyze metrics and detect anomalies with minimal human intervention.
  
  

• Datadog is a comprehensive cloud monitoring service that packages infrastructure, logging, network, user and security monitoring together. Datadog has maintained its position as a market leader by continually expanding its capabilities and integrations. Its Watchdog AI automatically detects performance and security issues, reducing alert fatigue and speeding up problem resolution.
  
  

• Dynatrace is an AI-powered, full-stack observability platform that has become increasingly popular for its automatic and intelligent monitoring capabilities. Its Davis AI engine provides actionable insights and automates root cause analysis, setting it apart in the market. Dynatrace's AI can automatically discover and map complex environments, making it ideal for large, dynamic cloud infrastructures.
  
  

• Google Cloud Monitoring has seen significant enhancements in its AI capabilities and is now a more complete option for those using Google Cloud. It offers deep integration with Google Cloud services and provides powerful analytics capabilities. Its service monitoring feature uses AI to help understand and optimize application performance and UX.
  
  

• New Relic is a powerful, full-stack observability platform that has gained significant traction. It offers a unified approach to monitoring applications, infrastructure and UX, making it easier for teams to correlate data across their entire tech stack. New Relic's AIOps capabilities use ML to automate anomaly detection and incident correlation.
  
  

These advanced cloud monitoring tools have become indispensable for IT professionals in 2024. However, they are just part of a comprehensive cloud monitoring strategy. To truly benefit from these tools, organizations must have skilled personnel who can effectively configure and deploy them, interpret the insights generated and make informed decisions based on the data provided.

As cloud environments become more complex, the importance of choosing the right monitoring tool and implementing it effectively cannot be overstated. These tools not only help in maintaining optimal performance and security, but also play a crucial role in cost optimization and capacity planning in the ever-evolving cloud landscape. While AI and ML enhance these capabilities, the fundamental value lies in providing clear, actionable insights into your cloud infrastructure.

Choosing the right cloud monitoring provider

When selecting a cloud monitoring provider, focus on your organization's specific needs and infrastructure. Evaluate providers based on their ability to offer comprehensive visibility across your entire cloud stack. Key factors include scalability, ease of integration with existing tools and the learning curve for your team.

Look for products that provide real-time alerting, customizable dashboards and strong reporting capabilities. Security features and compliance support should be top priorities, especially for sensitive data or regulated industries. Consider the provider's data retention policies, and ensure they align with your compliance requirements.

While advanced features can be beneficial, prioritize strong core functionality that matches your immediate monitoring needs. Compare pricing models to ensure good value without hidden costs as you scale. Finally, assess the quality of customer support, including documentation and training resources. The right provider should balance functionality, usability and value.

Adam Bertram is a 20-year veteran of IT and an experienced online business professional. He's an entrepreneur, IT influencer, Microsoft MVP, blogger, trainer and content marketing writer for multiple technology companies.