Last week I was in progress of adjusting our video application to retrieve video from separate MongoDB database servers. However, we discussed and instead decided to replace that with a single auto-scaling Amazon S3 bucket to serve our database needs. This will let us more easily upload new videos and verify and database integration errors on AWS consoles. This past week I completed the app adjustment to retrieve video from S3. Unfortunately, the AWS node.js function suite did not fully follow the structure of our previous video chunk retrieval framework. I cut out that framework so that we could have working deployment but unfortunately the video currently fully preloads and does not support scrubbing. I will be fixing that issue over the following week.
I also did some research on node-http-proxy as a module to enable our simple and lightweight load balancers, specifically the random choice and round robin (set sequential choice). Both of these load balancers can utilize high-level proxy functions for a fully pass-through request-response architecture in which the contents of the stream do not need to be read. I completed locally working versions of these load balancers that could each proxy for 10 different AWS front-end servers described in the first paragraph. I’m currently working on extracting front-end response times for our first reinforcement learning load balancers. I plan to build a latancy-based framework around this response time to complete our first reinforement learning algorithm by next week.