AWS Monitoring Tools for AWS Cloud Instances

Optimize performance with high-visibility, proactive AWS monitoring

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Fully functional for 30 days

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Get Actionable Insights

Auto-Discover Instances

Configure AWS Monitoring

Support Hybrid Clouds

Troubleshoot Performance

Get actionable insights for your AWS instances from a unified dashboard

SolarWinds^® Server & Application Monitor (SAM) includes AWS cloud monitoring tools built to provide visibility into your Amazon Elastic Compute Cloud (EC2) instances from a single pane of glass. The AWS monitoring tools in SAM include intuitive charts and non-intrusive architecture, complete with a web interface to help support straightforward cloud integration. The CloudWatch API can gather and display metrics in the Orion^® Platform and—in case you plan to poll additional metrics—allows you to monitor your AWS cloud as a node. You can also terminate, stop, or reboot any AWS instance monitored as a node.

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Fully functional for 30 days

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Auto-discover instances for comprehensive AWS monitoring

Automatically discover and view all instances deployed in your AWS account. The AWS cloud monitoring tools in SAM are built to find all EC2 cloud instances and Amazon Elastic Block Store (EBS) volumes in your cloud infrastructure, immediately start monitoring, and alert you to new instances. SAM provides multi-cloud monitoring for AWS by polling service APIs for performance and status metrics and aggregating the AWS monitoring data to help you monitor, manage, and troubleshoot issues. SAM consolidates a range of monitoring data, helping eliminate the need for multiple AWS monitoring tools.

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Fully functional for 30 days

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Monitor relevant performance metrics and configure alerts for cloud issues

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The AWS monitoring tools in SAM provide a comprehensive view of how your cloud services and applications are affecting overall performance. The AWS application performance monitoring features also allow you to automatically send alerts to the right stakeholder for problems like AWS throttling, AWS exceeded limits, polling issues, health status, and other thresholds. Though the default settings can provide visibility and notifications for critical and warning thresholds, SAM also allows you to customize monitoring and alerts to suit the needs of your IT environment.

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Fully functional for 30 days

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Support a hybrid environment with multi-cloud monitoring for AWS

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SAM is designed to help seamlessly integrate AWS application performance monitoring and can provide ongoing insight into on-premises and cloud server metrics for better mapping of your complete hybrid IT environment. SAM consolidates data from cloud-based AWS monitoring with information collected from on-premises and VM services to create a unified view of resources, applications, and services all in the same dashboard. This allows you to more easily compare and correlate metrics and performance issues.

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Fully functional for 30 days

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Troubleshoot cloud performance more efficiently with AWS application monitoring tools

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The AWS cloud monitoring tools in SAM offer a simplified approach to monitoring cloud health metrics and correlating historical performance data across managed applications. With the Performance Analysis Dashboard feature, or PerfStack^™, you can incorporate additional data from other Orion Platform-managed nodes into charts and graphs for visual overviews alongside cloud metrics. This can provide a critical overview of cloud performance issues, allowing you to drag and drop relevant performance data on adjustable timelines for a better understanding of how and when the issue occurred.

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Get More on AWS Monitoring

Do you find yourself asking…

What is AWS monitoring?
What is AWS hybrid cloud monitoring?
What are the benefits of AWS monitoring for DevOps?
What is monitored in AWS services?
What are metrics in AWS?
How does AWS monitoring work in SAM?

AWS is a commonly used cloud service provider offering a range of infrastructure, platform, and software as a service solutions. AWS monitoring involves maintaining, troubleshooting, and tuning the performance of AWS cloud instances.

An AWS monitoring tool designed to constantly monitor systems, applications, and other cloud-based environments can help you quickly respond to issues and optimize performance across cloud instances.

AWS systems and services can be significant components within highly complex infrastructures and environments, so it’s critical for you to ensure their stability, reliability, and uptime. With a professional monitoring tool, you can improve the efficiency of your AWS processes by tracking key metrics and activity capable of indicating new or worsening problems. By using proactive monitoring to identify these issues before they significantly affect performance, you can troubleshoot and resolve issues more efficiently.

In addition, your AWS monitoring strategy should include a comprehensive, holistic approach to gathering data from a wide spread of sources and nodes. This helps provide deeper visibility across the environment, which is especially beneficial when attempting to troubleshoot multi-point failures.

However, properly integrating, managing, and monitoring a hybrid infrastructure often comes with increased complexity. Successful AWS monitoring for hybrid computing environments should include tools and software capable of providing flexible, scalable solutions for a variety of infrastructure types. Since hybrid environments can include services based in public or private clouds, ensuring API compatibility, network connectivity, and seamless integrations between providers is critical. For instance, managing different service providers can be tricky, as you need to be able to manage issues in relation to varying service-level agreements (SLAs) while ensuring end users can access applications, services, and other resources.

An AWS monitoring tool can help you more effectively track and remedy issues by providing visibility of the full IT environment, from the AWS cloud to on-premises devices.

You can also improve AWS monitoring by establishing clear team responsibilities in case of specific events, incorporating automation into monitoring processes whenever possible, and ensuring you monitor Amazon Elastic Compute Cloud (EC2) instance log files. Choosing the right AWS monitoring tool can help streamline many of these important tasks.

AWS includes a wide range of services you can most effectively monitor by prioritizing specific performance metrics.

To monitor specific AWS services, SolarWinds^® Server & Application Monitor (SAM) allows you to create custom monitors using an API poller to collect data from Amazon EC2 environments like Elastic Block Store (EBS), Elastic Load Balancing (ELB), Relational Database Service (RDS), and ElastiCache.

Amazon EC2 instances are virtual servers capable of scaling to ensure companies and organizations have access to sufficient resources and capacity to operate business-critical applications. EC2 instances often require constant monitoring, as their performance can indicate deeper infrastructural issues.

Primary metrics to monitor for EC2 include the following:

CPU Utilization: This tracks the proportion of allocated compute units each EC2 instance is using, which can help identify resource bottlenecks or whether instances and resources are optimally provisioned for your environment’s workload.
DiskReadBytes and DiskWriteBytes: These track the data bytes being read from and written to EC2 instances, which can be used to pinpoint issues on the application level.
StatusCheck Failed: This metric is important for monitoring the health of EC2 instances and can provide information about whether a problem is caused by a specific instance or its supporting infrastructure.

Amazon EBS provides EC2 instances with long-term storage, allowing for efficient copying and transferring of replicas as infrastructure scales up. Metrics to monitor for EBS include the following:

VolumeReadBytes and VolumeWriteBytes: These track the data bytes copied to and from EBS volumes over a specified period of time, which is useful for determining your overall EBS load.
VolumeTotalReadTime and VolumeTotalWriteTime: These track the duration of read and write operations over a specified period—they’re especially useful for troubleshooting latency issues when correlated with throughput metrics.
VolumeQueueLength: This tracks the number of queued operations, which also helps grant visibility into total EBS workload. High volume queue length over time can contribute to increased latency.
VolumeIdleTime: This measures the duration of EBS volume inactivity, which can be leveraged to prevent costly and inefficient overprovisioning.
VolumeStatus: This metric helps monitor the health of EBS volumes, which can send warning statuses if underperforming.

Amazon ELB routes application traffic through multiple EC2 instances to prevent overloading. This has the benefit of boosting application health and fault tolerance by automatically distributing requests away from underperforming instances. Monitoring ELB health and performance metrics is essential to optimizing the end-user experience. Examples of ELB metrics to monitor include the following:

RequestCount: This tracks the total requests distributed over a specified period. Sudden spikes or drop-offs are often initial signs of an AWS- or DNS-related issue.
SurgeQueueLength: This tracks the queued requests ELB has yet to distribute. As with volume queue length, a high value for this metric over an extended period can lead to greater latency and worse performance. This is also important to monitor because once the queue capacity reaches its maximum cap, any incoming requests will be lost.
Latency: Rather than tracking load balancer latency, this metric tracks the time elapsing for back-end instances to respond to the requests ELB distributes.
HealthyHostCount and UnHealthyHostCount: These health checks help ELB to assess which instances can respond to requests and which need attention.

Amazon RDS allows for easier provisioning and operation of cloud-based database systems. To be most effective, AWS monitoring strategies should track the following RDS metrics:

FreeStorageSpace: This tracks the available allocated storage space for each database instance. Ensuring instances have sufficient storage space is key to preventing data loss and other application issues.
DatabaseConnections: This metric counts the open database connections over a specified period, which can be used to avoid hitting the maximum connection limit for each database engine and instance.
ReadLatency and WriteLatency: These track the average time elapsing for disk input/output requests, which can be leveraged to highlight under-provisioned resources.
DiskQueueDepth: This tracks the number of queued input/output requests. When monitored in correlation with latency, this metric provides insight into potential bottlenecks in the storage layer.

Amazon ElastiCache is an in-memory cache service enabling applications to access files and resources from the cache, removing the need to query back-end instances. This has the additional effect of increasing throughput and reducing latency for read-intensive loads. AWS CloudWatch performance metrics for ElastiCache you should consider monitoring include:

Current Connections: This tracks the total number of connections between clients and the cache. Unexpected drastic changes in this metric can be potential signs of issues with the underlying infrastructure.
Number of Set/Get Commands Processed: These metrics are indicators of throughput and cache usage, which can be useful for troubleshooting latency-related problems.
Cache Hits and Cache Misses: These can be cross-referenced to determine the rate of successful cache lookups. If hit rates are skewing low, it might be a sign additional cache resources need to be provisioned.
Evictions: This count refers to the items deleted from the cache to allow new files to be written. A consistently high number of evictions—as with a low hit rate—is a sign the cache size needs to be increased.
Swap Usage: This calculates the disk usage of cached data that should actually be stored in memory. The entire benefit of in-memory caching is predicated on a low swap usage, so this metric is crucial to monitor.

AWS is a commonly used cloud service provider offering a range of infrastructure, platform, and software as a service solutions. AWS monitoring involves maintaining, troubleshooting, and tuning the performance of AWS cloud instances.
An AWS monitoring tool designed to constantly monitor systems, applications, and other cloud-based environments can help you quickly respond to issues and optimize performance across cloud instances.
AWS includes a wide range of services you can most effectively monitor by prioritizing specific performance metrics.
To monitor specific AWS services, SolarWinds^® Server & Application Monitor (SAM) allows you to create custom monitors using an API poller to collect data from Amazon EC2 environments like Elastic Block Store (EBS), Elastic Load Balancing (ELB), Relational Database Service (RDS), and ElastiCache.
Amazon EC2 instances are virtual servers capable of scaling to ensure companies and organizations have access to sufficient resources and capacity to operate business-critical applications. EC2 instances often require constant monitoring, as their performance can indicate deeper infrastructural issues.
Primary metrics to monitor for EC2 include the following:
CPU Utilization: This tracks the proportion of allocated compute units each EC2 instance is using, which can help identify resource bottlenecks or whether instances and resources are optimally provisioned for your environment’s workload.
DiskReadBytes and DiskWriteBytes: These track the data bytes being read from and written to EC2 instances, which can be used to pinpoint issues on the application level.
StatusCheck Failed: This metric is important for monitoring the health of EC2 instances and can provide information about whether a problem is caused by a specific instance or its supporting infrastructure.
Amazon EBS provides EC2 instances with long-term storage, allowing for efficient copying and transferring of replicas as infrastructure scales up. Metrics to monitor for EBS include the following:
VolumeReadBytes and VolumeWriteBytes: These track the data bytes copied to and from EBS volumes over a specified period of time, which is useful for determining your overall EBS load.
VolumeTotalReadTime and VolumeTotalWriteTime: These track the duration of read and write operations over a specified period—they’re especially useful for troubleshooting latency issues when correlated with throughput metrics.
VolumeQueueLength: This tracks the number of queued operations, which also helps grant visibility into total EBS workload. High volume queue length over time can contribute to increased latency.
VolumeIdleTime: This measures the duration of EBS volume inactivity, which can be leveraged to prevent costly and inefficient overprovisioning.
VolumeStatus: This metric helps monitor the health of EBS volumes, which can send warning statuses if underperforming.
Amazon ELB routes application traffic through multiple EC2 instances to prevent overloading. This has the benefit of boosting application health and fault tolerance by automatically distributing requests away from underperforming instances. Monitoring ELB health and performance metrics is essential to optimizing the end-user experience. Examples of ELB metrics to monitor include the following:
RequestCount: This tracks the total requests distributed over a specified period. Sudden spikes or drop-offs are often initial signs of an AWS- or DNS-related issue.
SurgeQueueLength: This tracks the queued requests ELB has yet to distribute. As with volume queue length, a high value for this metric over an extended period can lead to greater latency and worse performance. This is also important to monitor because once the queue capacity reaches its maximum cap, any incoming requests will be lost.
Latency: Rather than tracking load balancer latency, this metric tracks the time elapsing for back-end instances to respond to the requests ELB distributes.
HealthyHostCount and UnHealthyHostCount: These health checks help ELB to assess which instances can respond to requests and which need attention.
Amazon RDS allows for easier provisioning and operation of cloud-based database systems. To be most effective, AWS monitoring strategies should track the following RDS metrics:
FreeStorageSpace: This tracks the available allocated storage space for each database instance. Ensuring instances have sufficient storage space is key to preventing data loss and other application issues.
DatabaseConnections: This metric counts the open database connections over a specified period, which can be used to avoid hitting the maximum connection limit for each database engine and instance.
ReadLatency and WriteLatency: These track the average time elapsing for disk input/output requests, which can be leveraged to highlight under-provisioned resources.
DiskQueueDepth: This tracks the number of queued input/output requests. When monitored in correlation with latency, this metric provides insight into potential bottlenecks in the storage layer.
Amazon ElastiCache is an in-memory cache service enabling applications to access files and resources from the cache, removing the need to query back-end instances. This has the additional effect of increasing throughput and reducing latency for read-intensive loads. AWS CloudWatch performance metrics for ElastiCache you should consider monitoring include:
Current Connections: This tracks the total number of connections between clients and the cache. Unexpected drastic changes in this metric can be potential signs of issues with the underlying infrastructure.
Number of Set/Get Commands Processed: These metrics are indicators of throughput and cache usage, which can be useful for troubleshooting latency-related problems.
Cache Hits and Cache Misses: These can be cross-referenced to determine the rate of successful cache lookups. If hit rates are skewing low, it might be a sign additional cache resources need to be provisioned.
Evictions: This count refers to the items deleted from the cache to allow new files to be written. A consistently high number of evictions—as with a low hit rate—is a sign the cache size needs to be increased.
Swap Usage: This calculates the disk usage of cached data that should actually be stored in memory. The entire benefit of in-memory caching is predicated on a low swap usage, so this metric is crucial to monitor.