What is Amazon EC2?

Amazon’s Elastic Compute Cloud (EC2) is used by 84% of companies on AWS. It enables teams to allocate compute resources rapidly and easily meet demand for truly web-scale performance. However, despite Amazon EC2’s resilience and elasticity, there are still ongoing objectives that require close monitoring of capacity, predictability, and interdependence with other services and infrastructure.

Limitations of Amazon CloudWatch

Although CloudWatch is a popular monitoring tool for AWS by default, it is insufficient for monitoring EC2 at any scale. It only provides metrics like CPU and memory, but not insights into your application layer. CloudWatch’s standard two weeks of retention doesn’t allow you to look back months ago to see changes over time. CloudWatch only offers simple dashboard widgets with a single metric and static alert thresholds, while most teams and modern applications require more advanced analytics.

Amazon EC2 Monitoring


Amazon EC2 Monitoring

SignalFx provides real-time cloud monitoring and intelligent alerting on AWS metrics as they stream from EC2, aggregated with metrics from the rest of the services in your environment, retained for 13 months. You also get a built-in Amazon EC2 monitoring dashboard right out of the box so you can see the metrics that matter to performance without guesswork or painful trial-and-error.

Resource Starvation: Servers can become unavailable when the resources that they need are exhausted. If a server does not even have enough memory to support an incoming SSH connection, you will not be able to access it through a remote terminal. SignalFx automatically derives metrics in its built-in Amazon EC2 dashboard to determine your remaining capacity based on consumption trends.

Usage & Performance: The performance of the server you’re running on determines application performance. It’s important to continuously monitor system performance because it’s often most affected during peak demand. You’ll also want to drill down into performance data across dimensions to determine the root cause of problems and address bottlenecks: Top Images by CPU %, Disk Metrics 24 H %, Top Network Bytes In & Out Per Minute, etc.

The SignalFx Difference

CPU Burst Credits: When you have spikes or temporary needs for CPU processing, you can use your burst credits to process the data quickly. If you need more CPU on a steady-state basis, you will need to increase your instance size. SignalFx also automatically tracks the number of network packets read and written over the network interface.

Analytics & Derived Metrics:  By visualizing the data in multiple ways and combining time series as derived metrics with SignalFx, you can pinpoint the data streams that matter and set dynamic alert thresholds from the built-in list of recommended detectors. Percentages are useful when you want to see whether a server is running below its maximum capacity or is at risk of running out of memory. For example, the ratio of memory used to total memory, shown as percent memory used, provides a quick visual of the distribution of memory usage across all servers for the EC2 service and how it changes over time.

Amazon EC2 Metrics

CPU Credit Balance
CPU Credit Usage
Active Hosts
Active Hosts by 
Instance Type
Active Hosts by
Availability Zone
Top Instances
by CPU %
Top Images
by CPU %
Disk Ops/Min
Disk I/O 
Disk Metrics 
24 H Growth %
Network Bytes In
Network Bytes Out
Top Network 
Bytes In/Min
Top Network
Bytes Out/Min
Network Bytes In 
vs. 24 H Change %
Network Bytes Out 
vs. 24 H Change %

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