Hi Asiri, Great work on the proposal. I have some few concerns/suggestions.
RIF metric is calculated by taking the number of requests currently in the LB's queue, AFAIK. Therefore, rather than taking input for rif count that an instance could handle, it would make sense to calculate the number of instances required to maintain the average RIF. For eg. let's say we have 2 instances, and RIF avg is 150, and predicted RIF goes to 170. It means using 2 instances, one instance may have to take 85 RIF. But avg RIF for one instance should ideally be 75. Then we can calculate how many instances we need to maintain 75 RIF per instance. This is merely a suggestion. Reason is I don't think taking user input for RIF per instance would make much sense, IMHO. Thanks. On Thu, Jun 5, 2014 at 9:51 AM, Asiri Liyana Arachchi <asiriw...@gmail.com> wrote: > 1. Improve the auto-scaling to predict the number of instances needed. > > Starting a new thread with suggestions to predict the number of instances. > > There are three factors that are being considered when auto scaling. > Requests in flight (rif) > Memory Consumption > Load average. > > For requests in flight. > > User input - Number of rif than an instance could handle. > > Once it's given we can simply calculate the required number of instances to > spawn or terminate. > > For an e.g. > Number of rif that an instance could handle - 50 > Predicted rif =170 > Required instances = 170 /50 > = 4 (taking the ceiling value ) > > If the current number of instances is 2 another 4-2 have to be spawned. > If the current number of instances is 6 , the number of instances that > should be terminated is 4-6 > > When rounding of values ( number of instances ) we can either follow the way > amazon did it for percentage based auto scaling [1] or we can let user > decide (in autoscaling policy) whether to use ceiling or floor value to > round off depending on his server availability requirements. Welcome your > thoughts on this. > > Here is the project's work that i'm supposed to complete. > > 1) setting up apache stratos on openstack. > 2) research on how to use load average / memory consumption for instance > calculation. > 3) Getting community feed back and implementation. > 4) Research on improving prediction algorithm. > 5) Schedule based autoscaling. > > Currently working on setting up apache stratos.(for testing) > > [1] > http://docs.aws.amazon.com/AutoScaling/latest/DeveloperGuide/as-scale-based-on-demand.html -- Akila Ravihansa Perera Software Engineer WSO2 Inc. http://wso2.com Phone: +94 77 64 154 38 Blog: http://ravihansa3000.blogspot.com
