EC2 Container - ECS Test

AWS users can opt to run instances within Elastic Compute Cloud () or look into using containers. Amazon Container Service (ECS) manages Docker containers within AWS, allowing users to easily scale up or down and evaluate and monitor CPU usage. These AWS containers run on a managed cluster of instances, with ECS automating installation and operation of the cluster infrastructure. The first step to get started with ECS therefore is to create a cluster and launch instances in it. Then, create task definitions. A task is one or more Docker containers running together for one service or a microservice. When configuring a container in your task definition, you need to define the container name and also indicate how much memory and how many CPU units you want to reserve for each container. Finally, you will have to create a service, so that you can run and maintain a specified number of instances of a task definition simultaneously.

Time and again, administrators will have to check on the resource usage of each cluster, so that they can identify those clusters that have been consistently over-utilizing the CPU and memory resources. Resource usage at the individual service-level should also be monitored, so that administrators can figure out whether the excessive resource consumption by a cluster is because the cluster itself does not have enough resources at its disposal, or because one/more services running on the cluster are depleting the resources. Using the EC2 Container - ECS test, administrators can monitor resource usage both at the cluster and the service-level.

This test auto-discovers the clusters configured in the region being monitored and also the services running on each cluster. CPU and memory usage is then reported for each cluster and service, alongside the CPU and memory reservations (of all tasks) per cluster. These insights help administrators understand where there is a contention for resources - at the cluster-level? or at the service-level? or both? - and accordingly decide what needs to be done to optimize resource usage:

• Should more container instances be added to the cluster to increase the amount of resources at its disposal?
• Should the task definitions of the resource-hungry services be fine-tuned so that the service has more resources to use?

Target of the test: Amazon Region

Agent deploying the test: A remote agent

Output of the test: One set ofresults for each cluster:service pair in the monitored region

Configurable parameters for the test

Parameter	Description
Test Period	How often should the test be executed.
Host	The host for which the test is to be configured.
AWS Access Key, AWS Secret Key, Confirm AWS Access Key, Confirm AWS Secret Key	To monitor an Amazon instance, the eG agent has to be configured with the access key and secret key of a user with a valid AWS account. For this purpose, we recommend that you create a special user on the AWS cloud, obtain the access and secret keys of this user, and configure this test with these keys. The procedure for this has been detailed in the Obtaining an Access key and Secret key topic. Make sure you reconfirm the access and secret keys you provide here by retyping it in the corresponding Confirm text boxes.
Proxy Host and Proxy Port	In some environments, all communication with the AWS cloud and its regions could be routed through a proxy server. In such environments, you should make sure that the eG agent connects to the cloud via the proxy server and collects metrics. To enable metrics collection via a proxy, specify the IP address of the proxy server and the port at which the server listens against the Proxy Host and Proxy Port parameters. By default, these parameters are set to none , indicating that the eG agent is not configured to communicate via a proxy, by default.
Proxy User Name, Proxy Password, and Confirm Password	If the proxy server requires authentication, then, specify a valid proxy user name and password in the proxy user name and proxy password parameters, respectively. Then, confirm the password by retyping it in the CONFIRM PASSWORD text box. By default, these parameters are set to none, indicating that the proxy sever does not require authentication by default.
Proxy Domain and Proxy Workstation	If a Windows NTLM proxy is to be configured for use, then additionally, you will have to configure the Windows domain name and the Windows workstation name required for the same against the proxy domain and proxy workstation parameters. If the environment does not support a Windows NTLM proxy, set these parameters to none.
Exclude Region	Here, you can provide a comma-separated list of region names or patterns of region names that you do not want to monitor. For instance, to exclude regions with names that contain 'east' and 'west' from monitoring, your specification should be: *east*,*west*
ECS Filter Name	By default, this test reports metrics for each cluster and for each service that is running on a cluster. Accordingly, ServiceName is the default selection from the ECS Filter drop-down. If you do not want service-level metrics, then you can configure the test to report resource usage at the cluster-level alone. For this, just select ClusterName from the ECS Filter drop-down. If this is done, then the test will only report cluster names as descriptors.

Measures reported by the test:

Measurement	Description	Measurement Unit	Interpretation
CPU utilization:	Indicates the percentage of CPU units used by this cluster or by this service	Percent	For a cluster, this value is computed using the following formula: Total CPU units currently used by ECS tasks on this cluster / Total CPU units that were registered for all the container instances in this cluster * 100 A value close to 100% for this measure at the cluster-level could either indicate that the cluster is resource-starved or that one/more services running on the cluster are consuming excessive resources. If the reason for high CPU usage is the poor resource configuration of the cluster, then, you may want to add more instances to the cluster to add to its resource base. On the other hand, if the cluster is adequately sized with CPU, then you may want to check the value of this measure for each of the services running on the cluster . For a service, this value is computed using the following formula: Total CPU units currently used by ECS tasks defined for this service / Total CPU units that are reserved for the tasks defined for this service * 100 Compare the value of this measure across services of a cluster to know which services of that cluster are guilty of over-utilization of CPU. Once the services are identified, check the CPU reservation of the task definitions of those services to determine whether sufficient resources have been allocated to those tasks. If not, increase the reservations to allow optimal resource usage.
Memory utilization:	Indicates the percentage of memory used by this cluster or by this service	Percent	For a cluster, this value is computed using the following formula: Total memory currently used by ECS tasks on this cluster / Total memory that is registered for all the container instances in this cluster * 100 A value close to 100% for this measure at the cluster-level could either indicate that the cluster is resource-starved or that one/more services running on the cluster are consuming excessive resources. If the reason for high memory usage is the poor resource configuration of the cluster, then, you may want to add more instances to the cluster to add to its resource base. On the other hand, if the cluster is adequately sized with memory, then you may want to check the value of this measure for each of the services running on the cluster . For a service, this value is computed using the following formula: Total memory currently used by ECS tasks defined for this service / Total memory reserved for the tasks defined for this service * 100 Compare the value of this measure across services of a cluster to know which services of that cluster are guilty of over-utilization of memory. Once the services are identified, check the memory reservation of the task definitions of those services to determine whether sufficient resources have been allocated to those tasks. If not, increase the reservations to allow optimal resource usage.