|By Mark O'Neill||
|June 30, 2014 06:56 PM EDT||
This is often done because an organization wishes to deploy API Gateways virtually in multiple places on the network, while using the same server to run the API Gateways.
To do this, the first step is to associate the multiple IP Addresses with the machine running the API Gateway. In this example, we associate two IP Addresses (with two corresponding subnets): 188.8.131.52 and 10.10.1.10. This is done at the OS layer.
Next, we use Policy Studio to setup our two listeners, corresponding to the two zones (which we'll imaginatively call "Zone 1" and "Zone 2"):
Note where the IP addresses are configured above. Users of the API Gateway might be used to seeing an asterix ('*') in the "Address" field under the port configuration. The asterix means that the API Gateway binds to every IP address available on the machine. By specifying the IP address in the "Address" field, we are saying that the API Gateway will only bind to this port for this listener.
Also notice above that both listeners are listening on the same port, which is the SSL port 443. Normally if you have two applications listening on the same port, there is a clash. But in this case, the API Gateway is listening on two ports on two different IP addresses.
Underneath our "Zone 1" and "Zone 2" listeners, we can associate different paths. So, https://184.108.40.206/myAPI will be handled under the "Zone 1" listener.
Notice also that different certificates can be used for the different listeners. The certificates themselves can be generated using the Axway API Management solution (under "Certificates and Keys" in Policy Studio). If you have multi-homed your API Gateway to multiple addresses which are associated with multiple machine names (e.g. "apis.mycompany.com" and "internal.mycompany.com") then you can issue certificate within Policy Studio for these names, then load them using the "X.509 Certificate" button in the "Configure HTTPS Interface" screen above.
Happy multi-homing! There's no place like a (multi)home :-)
Sep. 29, 2016 10:30 PM EDT Reads: 4,021
Sep. 29, 2016 10:15 PM EDT Reads: 2,786
Sep. 29, 2016 10:00 PM EDT Reads: 1,804
Sep. 29, 2016 09:45 PM EDT Reads: 3,126
Sep. 29, 2016 08:45 PM EDT Reads: 1,546
Sep. 29, 2016 08:45 PM EDT Reads: 2,209
Sep. 29, 2016 06:15 PM EDT Reads: 3,685
Sep. 29, 2016 06:00 PM EDT Reads: 1,537
Sep. 29, 2016 05:15 PM EDT Reads: 2,855
Sep. 29, 2016 05:15 PM EDT Reads: 1,583
Sep. 29, 2016 04:45 PM EDT Reads: 2,794
Sep. 29, 2016 04:45 PM EDT Reads: 3,446
Sep. 29, 2016 04:30 PM EDT Reads: 1,343
Sep. 29, 2016 04:30 PM EDT Reads: 1,962
So, you bought into the current machine learning craze and went on to collect millions/billions of records from this promising new data source. Now, what do you do with them? Too often, the abundance of data quickly turns into an abundance of problems. How do you extract that "magic essence" from your data without falling into the common pitfalls? In her session at @ThingsExpo, Natalia Ponomareva, Software Engineer at Google, provided tips on how to be successful in large scale machine learning...
Sep. 29, 2016 04:00 PM EDT Reads: 2,402