Serverless computing has become the next level of public cloud service. While approaches to monitoring application performance and cloud infrastructure are well established, cloud network monitoring continues to be a challenge for enterprises.
Top 10 Cloud Monitoring Tools in 2021
The main challenge today in public clouds is providing security and gaining access to data streams for data collection.
If you are considering moving to a serverless architecture, the tools in our article will help you with collecting that data, identifying/eliminating threats, and building a more productive and cost-effective cloud infrastructure.
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About Cloud Monitoring
Cloud providers offer users access to infrastructure performance metrics. However, some customers require more granular data than providers can offer.
Deploying agents from the infrastructure monitoring toolkit will allow such users to collect additional infrastructure performance data. They will also be able to combine it with data from non-cloud environments that workloads in the cloud can interact with.
Application performance monitoring will work similarly in both cloud and non-cloud environments when users use the toolkit to collect performance metrics for their code.
Thus, businesses have been successful in the cloud, relying on their chosen application performance monitoring and infrastructure monitoring tools. However, they are still looking for a better approach to monitoring the network in the cloud.
Therefore, analysts expect continued experiments in this area from vendors looking to protect their market positions while developing new approaches. The latter is supposed to be more responsive to user requirements related to visibility in the cloud.
Why Cloud Application Monitoring is Important
By using cloud applications with poor performance, companies lose revenue and customers. In fact, as complexity increases, cloud apps may have performance issues, resulting in lower conversions and higher bounce rates. Therefore, companies that launch cloud-based web apps need to constantly monitor their performance, functionality, and availability issues.
The Complexity of Managing Cloud Applications
Traditional web apps “sit” on static servers inside the data center behind advanced company firewalls. This delivery chain is fixed with minimal variables. Cloud applications, however, are more complex by nature.
In cloud applications, the app delivery chain moves from the end-user through the browser (PC or mobile device), through the Web (local Internet service provider (ISP), or mobile operator), through a third party (cloud service provider or content delivery network (CDN)), to the complex data center infrastructure.
All of these elements are essential to cloud app performance. This makes cloud testing and monitoring much more complex than traditional app performance management.
External Cloud Monitoring Simplifies Application Management
Solving cloud performance problems requires a comprehensive analysis of the entire application delivery chain, from the end-user to the data center servers. Critical to cloud monitoring is the ability to identify specific errors in the delivery chain.
When external cloud monitoring agents/tools are initialized, IT managers and system administrators can quickly and efficiently solve performance issues and use their apps to their full potential.
External cloud monitoring also saves considerable time and hassle of deploying code to a system or creating an integrated server monitoring tool.
Top 10 Cloud Monitoring Services & Tools
SenseDeep is a very robust serverless monitoring platform for AWS (Amazon Web Services). It helps users troubleshoot services with real-time call tracing.
It’s one of the fastest serverless monitoring platforms for AWS that also helps create and display lambda metrics.
IOPipe is a great monitoring platform with incident analytics for serverless apps. It supports everything, from monolithic to serverless architecture.
The company was bought by New Relic, so the platform is now part of its products. It supports Node, Java, and Python.
The platform doesn’t require access to AWS data, and there’s plenty of documentation on how to use it, also Cloudways offers new relic service too to its customers.
Plus, there’s a wide community that can help you get up to speed fast, no matter where you are located.
Amazon Cloudwatch is a metrics repository. It’s a monitoring service for cloud resources and apps you run on AWS.
If you run a serverless application, you can use the service to deploy metrics to it and then get statistics from them.
Use AWS CloudWatch to easily monitor and identify problems before they cause serious damage to your architecture.
Google Cloud Operations Suite
Google Cloud Operations Suite is a comprehensive cloud solution that lets you see the performance, availability, and overall condition of your apps and infrastructure.
The Google Cloud monitoring platform gives you access to a bunch of tools to help you monitor and debug apps hosted on GCP, and also supports AWS Lambda app monitoring.
Google Cloud gives you real-time log management and analysis of everything in the Cloud Operations Suite. Metrics, events, and metadata are displayed using an advanced query language. So you can identify patterns, troubleshoot, and eliminate problems.
In addition, Google supports frequent and quality documentation updates, as well as provides free cloud training materials.
Dashbird is an easy-to-use serverless monitoring platform. The main task of its developers was to create a truly logical and accurate service. And they did a great job.
Dashbird’s interface makes it easy to monitor and debug problems: it lets you better understand what’s going on with the lambda expressions and API gateways in your account.
Dashbird automatically collects data from your app. You only need to create dashboards via metrics to view events and filter them. The platform actively analyzes logs and metrics to find bugs and improve performance.
Dashbird is extremely useful for debugging the script you prepare by getting the logs themselves. You’ll see a detailed report of how your serverless solution is being used and receive notifications on occurred errors.
Splunk + SignalFx
Splunk’s SignalFx is a serverless application monitoring platform with powerful but simple log investigation mechanisms. It provides tools for tracking and rapid troubleshooting with automatic incident response.
Splunk shows information about your cloud applications and micro-services architecture with real-time visibility and performance monitoring of your features.
The SignalFX platform is suitable for experienced developers and engineers for software-defined data management, without being tied to a service provider.
Thundra is a serverless app-integrity and security-monitoring service. It separates execution data from lambda functionality and supports diagram reading, debugging, and end-to-end monitoring of serverless architecture.
Thundra offers detailed monitoring of a distributed system and does not increase latency.
The service makes extensive use of machine learning and data science. To understand application behavior patterns, Thundra identifies anomalies that are automatically blacklisted or other actions are taken.
Cloudwatch Logs makes it easy to set up and deploy an app. However, it requires access to AWS data.
Epsagon offers automated and distributed tracing in the most complex serverless environments. The service offers a single dashboard for services, an intuitive interface, and infrastructure components.
Epsagon also features correlations and serverless monitoring tools to help you conveniently visualize data to get the full picture of app performance.
Customizable logs and cloud management tools come in handy for evaluating app performance, which is necessary to quickly eliminate bottlenecks.
Epsagon is great for troubleshooting AWS Lambda, however, it still does not support multiple AWS accounts and is not mobile-optimized.
Sentry makes debugging efficient with Python and AWS Lambda to keep your serverless applications running reliably.
The service provides you with tools to view the runtime environment in real-time. Plus, you can use it to provide all the information you need for quick troubleshooting.
Sentry uses runtime tools to capture errors. This helps the user diagnose a problem and find its cause through stack tracing, navigation chains, environment context, and functions.
Sentry is perfect for beginners and companies that want to find function delays fast.
The service also provides distributed tracing in addition to error monitoring for serverless functions.
Serverless Framework PRO
With Serverless Framework Pro, you can do more than just monitor and troubleshoot your serverless applications.
This service provides tools to fully manage the lifecycle of your serverless application and automate everything: CI/CD with zero configuration, observability, troubleshooting, and more.
In the service, you can easily connect to your AWS and GitHub accounts and select repositories. After that, you’re ready to test and deploy your application.
Serverless Framework Pro is ideal both for small companies to optimize development and enthusiasts who don’t want to overpay.
About Cloud Services
Unlike local networks, where virtual ports can be configured to collect all packets, e.g. a virtual traffic sniffer, cloud networks do not provide this type of access. This is a gap that cloud services are only beginning to address.
Examples can be AWS’ VPC Flow Logs functionality and Microsoft’s Azure (currently available as TAP, i.e. the preview version). However, these capabilities are still very limited in use, so the decision to use them is risky and will inevitably incur additional costs.
The preferred method of collecting network data is to use agents, one for each virtual machine.
Unlike containers, virtual machines tend to be durable. So installing a lightweight agent to collect data will bypass the limitations in the cloud service and ensure continuous monitoring.
As with containerized environments, some products require a central data collection point, while other solutions are able to send data directly to the target software, providing analytics statistics.
One of the advantages of a centralized data collection virtual machine in Microsoft Azure is that it can be a collector for Azure’s TAP virtual network, a very simple service that currently offers no filtering or multi-directional delivery.
However, centralized collectors are essentially network packet brokers and can accept captured traffic from TAP. They will then process, filter, and forward it to one or more recipients.
We can only hope that if Azure’s TAP becomes popular, users will start to demand more features from the service, which will reduce the need for an intermediate network packet broker.
Currently, collecting and processing network performance and network security data in a cloud service is known to be the most successful experience. This reduces the cost of delivering the data elsewhere.
Even if this data is reduced to metadata, the cost is still much lower than sending raw packets over the network. This is the main challenge right now for vendors who want to be successful in monitoring the network in the cloud.
IT departments in companies will continue to face new challenges in collecting, processing, securing, and statistically analyzing their application performance data across containers, microservices, and cloud environments. The following questions remain open:
- How to collect performance data across environments?
- What tools to use to collect data, does it make sense to use the same toolkit?
- Wherever data processing and storage (depending on workload location) will be performed.
- How to ensure that data collection occurs as application workloads change?
IT professionals should see cloud monitoring as an experience that is adaptable to new environments and able to encompass all application deployment environments used by the business.
This means working closely with application architects and developers to implement network data collection and monitoring functionality as a core element of any new or migrated application to the cloud.
On the other hand, adding support for cloud and containerized environments is an important step in extending the functionality of the application and network performance monitoring vendor solutions.
This is also the reason why we see more and more free time tracking tools running on cloud servers.
While there are several ways to implement data collection, the result tends to be the same for all vendors. Some methods are better at meeting the specific needs of a particular enterprise compared to others.
However, this property will hold more weight for new sales, while remaining, from the consumer’s perspective, a weak motivation to replace or increase the number of monitoring solution providers.
By the way, have you heard of Nubeva? It’s a network data collection startup that takes a unique cloud-based approach. However, it lacks physical and local network data collection capabilities.
The ability to decrypt TLS for containers and Kubernetes, including support for TLS 1.3, is an important factor In choosing. Especially for those companies that establish higher requirements for accurate efficiency and performance measurement.
Nubeva is also a good acquisition target for network performance monitoring, application performance monitoring, and security monitoring vendors looking to implement their own network packet capture tools in the cloud.
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