Added README with instructions to run the Elasticsearch example

Author: ferdingler

Dockerfile

@@ -1,2 +1,3 @@
 FROM blazemeter/taurus
 ADD examples/http /bzt-configs/
+# ADD examples/elasticsearch /bzt-configs/

docs/elasticsearch.png

@@ -0,0 +1,66 @@
+### Load Testing an Amazon Elasticsearch Cluster
+
+This example assumes that your Elasticsearch cluster is behind a VPC and not exposed publicly to the internet. However, 
+either way is possible to test. Having it publicly accessible makes it even easier. 
+
+![Arch](../../docs/elasticsearch.png)
+
+To start, follow all the instructions 
+in the main [README](https://github.com/aws-samples/distributed-load-testing-using-aws-fargate/blob/master/README.md), 
+except for **Step 2 and 4** in which you need to do the following changes instead.
+
+#### Instead of Step 2
+Open the [Dockerfile](https://github.com/aws-samples/distributed-load-testing-using-aws-fargate/blob/master/Dockerfile) 
+in the root directory of the project and uncomment the elasticsearch instruction and comment out the http one. It should
+look like this: 
+
+```Dockerfile
+# ADD examples/http /bzt-configs/
+ADD examples/elasticsearch /bzt-configs/
+```
+
+Then, edit the [runner.py](https://github.com/aws-samples/distributed-load-testing-using-aws-fargate/blob/master/bin/runner.py) 
+script and specify your Elasticsearch cluster URL in the environment variable.
+
+```python
+ENDPOINT_UNDER_TEST = 'https://vpc-123456789.aws.us-west-2.amazon.com'
+```
+
+Finally, write your test scenarios. I have written a `consumer.test.js` and a `producer.test.js` scenarios for 
+demonstration purposes, but you can edit them to fit your needs. In this example, the producer will create random JSON
+documents to be indexed in the cluster, while the consumer will issue *search* requests of randomly generated words. 
+If you look at the `taurus.yml` file, you can see both scenarios specified, which means that Taurus will execute both 
+scripts in parallel.
+
+#### Instead of Step 4
+
+In the case of your Elasticsearch cluster running behind a VPC, the easiest option would be run the Fargate Docker tasks 
+in the same VPC. So, follow **Step 4** just as described in the main [README](https://github.com/aws-samples/distributed-load-testing-using-aws-fargate/blob/master/README.md) 
+but use the CloudFormation template located in `cloudformation/main-with-existing-vpc.yml`. This template will let you
+choose the VPC and Subnets to place the Fargate cluster. 
+
+For this same reason, run the CloudFormation template in 1 region only and make sure to edit the `bin/runner.py` script
+to reflect the one region where you created it. Which should be the same region where Elasticsearch is running. 
+
+```python
+regions = [
+    {
+      'name': 'us-east-1',
+      'stackName': 'dlt-fargate',
+      'taskCount': 3
+    },
+#    {
+#      'name': 'us-east-2',
+#      'stackName': 'dlt-fargate',
+#      'taskCount': 3
+#    },
+#    {
+#      'name': 'us-west-2',
+#      'stackName': 'dlt-fargate',
+#      'taskCount': 3
+#    }
+]
+```
+
+Remember, if your Elasticsearch cluster is publicly accessible, meaning that it's HTTP endpoint is reachable from anywhere 
+in the world, you can ignore the previous step and deploy the Fargate cluster in multiple regions and in it's own VPCs.