-
-
Save xandkar/5144503 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # you can make a text file of request times (in ms, one number per line) and import it here, or you can use a probability distribution to simulate request times (see below where setting req_durations_in_ms) | |
| # rq = read.table("~/Downloads/request_times.txt", header=FALSE)$V1 | |
| # argument notes: | |
| # parallel_router_count is only relevant if router_mode is set to "intelligent" | |
| # choice_of_two, power_of_two, and unicorn_workers_per_dyno are only relevant if router_mode is set to "naive" | |
| # you can only select one of choice_of_two, power_of_two, and unicorn_workers_per_dyno | |
| run_simulation = function(router_mode = "naive", | |
| reqs_per_minute = 9000, | |
| simulation_length_in_minutes = 5, | |
| dyno_count = 100, | |
| choice_of_two = FALSE, | |
| power_of_two = FALSE, | |
| unicorn_workers_per_dyno = 0, | |
| track_dyno_queues = FALSE, | |
| parallel_router_count = 1) { | |
| if(!(router_mode %in% c("naive", "intelligent"))) { | |
| return("router_mode must be one of 'naive' or 'intelligent'") | |
| } | |
| unicorn = as.numeric(unicorn_workers_per_dyno) > 0 | |
| if(sum(c(choice_of_two, power_of_two, unicorn)) > 1) { | |
| return("you can only set one of choice_of_two, power_of_two, and unicorn!") | |
| } | |
| reqs_per_ms = reqs_per_minute / 60000 | |
| simulation_length_in_ms = ceiling(simulation_length_in_minutes * 60000) | |
| # reqs_starting is a vector where reqs_starting[i] represents the number of requests that start at millisecond i | |
| reqs_starting = rpois(simulation_length_in_ms, reqs_per_ms) | |
| # total number of requests for duration of simulation | |
| total_requests = sum(reqs_starting) | |
| # req_durations_in_ms[i] represents the amount of time, in milliseconds, that request i will take to finish after a dyno starts working on it | |
| # req_durations_in_ms = sample(rq, total_requests, TRUE) | |
| req_durations_in_ms = ceiling(rweibull(n = total_requests, shape = 0.8, scale = 79.056)) | |
| # For our simulation we used an empirical distribution of request times observed from our server, but we also | |
| # found that the Weibull distribution with shape parameter = 0.46 is a reasonable approximation. | |
| # You can change the code below to use whatever distribution of request times you'd like | |
| # wshape = 0.46 | |
| # wlambda = 50 / (log(2) ^ (1 / wshape)) | |
| # req_durations_in_ms = pmin(30000, pmax(10, ceiling(rweibull(nreqs, wshape, wlambda)))) | |
| # the code below sets up the results matrix, which has one row for each request and will eventually have 4 columns of data: | |
| # 1) request start time | |
| # 2) request duration | |
| # 3) to which dyno was the request assigned? | |
| # 4) how much time, if any, did the request spend in queue between when the request arrived and when a dyno started working on it? | |
| # we can fill columns 1 and 2 based on results from above, and if we're in "naive" mode we can additionally fill column 3 | |
| # the rest of the code will be calculate the values for column 4 | |
| uniq_start_times = which(reqs_starting > 0) | |
| start_times = unlist(sapply(uniq_start_times, function(x) rep(x, reqs_starting[x]))) | |
| if(router_mode == "naive") { | |
| dyno_assignments = sample(1:dyno_count, total_requests, replace=TRUE) | |
| router_assignments = rep(NA, total_requests) | |
| } else { | |
| dyno_assignments = rep(NA, total_requests) | |
| router_assignments = sample(1:parallel_router_count, total_requests, replace=TRUE) | |
| } | |
| results = matrix(c(start_times, | |
| req_durations_in_ms, | |
| dyno_assignments, | |
| rep(0, total_requests), | |
| rep(0, total_requests), | |
| router_assignments), | |
| nrow = total_requests, | |
| ncol = 6, | |
| dimnames = list(1:total_requests, c("start_time", "request_duration", "dyno", "time_in_queue", "end_time", "router"))) | |
| # dyno_next_available[i] represents the next millisecond at which dyno i will be free to being working on a new request | |
| # for example, if dyno 1 gets a request at time = 100 ms and the request lasts 55 ms, then dyno_next_available[1] will be set to 155 | |
| dyno_next_available = rep(0, dyno_count) | |
| # have to track dyno queues if you want to do power of two | |
| # also might want to track dyno queues if, for example, you want to plot or animate the simulation | |
| if(power_of_two) track_dyno_queues = TRUE | |
| if(track_dyno_queues) { | |
| # dynomat[i,j] represents the number of requests assigned to dyno i at time j | |
| dynomat = matrix(0, nrow = dyno_count, ncol = simulation_length_in_ms) | |
| } | |
| if(router_mode == "naive" & unicorn) { | |
| dyno_next_available = rep(dyno_next_available, unicorn_workers_per_dyno) | |
| } | |
| if(router_mode == "intelligent") { | |
| # routermat[i,j] represents when router i thinks dyno j will next be available | |
| routermat = matrix(0, nrow = parallel_router_count, ncol = dyno_count) | |
| } | |
| for(i in 1:nrow(results)) { | |
| row = results[i,] | |
| st = row["start_time"] | |
| duration = row["request_duration"] | |
| if(router_mode == "naive") { | |
| dyno = row["dyno"] | |
| # if using the choice of two approach, check if the random dyno is busy, and if it is then pick another random dyno | |
| if(choice_of_two & dyno_next_available[dyno] > st) { | |
| dyno = sample(1:dyno_count, 1) | |
| results[i, "dyno"] = dyno | |
| } | |
| # if using power of two and the first dyno is busy, poll a second dyno and pick the one that has a shorter queue depth | |
| if(power_of_two & dyno_next_available[dyno] > st) { | |
| other_dyno = sample(1:dyno_count, 1) | |
| dyno_queue_depth = dynomat[dyno, st] | |
| other_dyno_queue_depth = dynomat[other_dyno, st] | |
| if(other_dyno_queue_depth < dyno_queue_depth) { | |
| dyno = other_dyno | |
| results[i, "dyno"] = dyno | |
| } | |
| } | |
| # if using unicorn, pick a dyno at random, but then assign the request to a worker on that dyno based on which worker first comes available | |
| if(unicorn) { | |
| sub_dynos = seq((dyno - 1) * unicorn_workers_per_dyno + 1, length = unicorn_workers_per_dyno) | |
| dyno = as.numeric(sub_dynos[which(dyno_next_available[sub_dynos] <= st)[1]]) | |
| } | |
| } | |
| else { | |
| # if we're in 'intelligent' mode, assign task to the first dyno that the router thinks is available (i.e. not working on some other request) | |
| router = row["router"] | |
| # important to pick randomly here instead of taking the lowest numbered dyno that is available | |
| # if we pick the first element then every router will start with dyno 1, causing unnecessary queuing | |
| dynos_to_choose_from = which(routermat[router,] <= st) | |
| dyno = ifelse(length(dynos_to_choose_from) > 0, sample(dynos_to_choose_from, 1), NA) | |
| } | |
| # if we've assigned a dyno and that dyno is available, then the request is not queued, and the dyno is tied up until the request is finished | |
| if(!is.na(dyno) && dyno_next_available[dyno] <= st) { | |
| dyno_next_available[dyno] = st + duration | |
| results[i, "end_time"] = st + duration - 1 | |
| if(router_mode == "intelligent") { | |
| routermat[router, dyno] = st + duration | |
| } | |
| if(track_dyno_queues) { | |
| t_ix = st:min(st + duration - 1, simulation_length_in_ms) | |
| dynomat[dyno, t_ix] = dynomat[dyno, t_ix] + 1 | |
| } | |
| } | |
| # otherwise the request will be queued | |
| else { | |
| # 'intelligent' queueing will assign the request to the next dyno that comes available | |
| if(is.na(dyno) && router_mode == "intelligent") { | |
| routerrow = routermat[router,] | |
| dyno = sample(which(routerrow == min(routerrow)), 1) | |
| # again have to sample here instead of which.min() | |
| } | |
| if(router_mode == "naive" & unicorn) { | |
| dyno = as.numeric(sub_dynos[which.min(dyno_next_available[sub_dynos])]) | |
| } | |
| queue_time = dyno_next_available[dyno] - st | |
| results[i, "time_in_queue"] = queue_time | |
| results[i, "end_time"] = st + queue_time + duration - 1 | |
| dyno_next_available[dyno] = st + queue_time + duration | |
| if(router_mode == "intelligent") { | |
| routermat[router, dyno] = st + queue_time + duration | |
| } | |
| if(track_dyno_queues) { | |
| t_ix = st:min(st + queue_time + duration - 1, simulation_length_in_ms) | |
| dynomat[dyno, t_ix] = dynomat[dyno, t_ix] + 1 | |
| } | |
| } | |
| } | |
| return(results) | |
| } | |
| frac_queued = function(result) { | |
| qtimes = result[, "time_in_queue"] | |
| data.frame(frac_queued = round(mean(qtimes > 0), 3), | |
| mean_queue_time_when_queued = round(mean(qtimes[qtimes > 0])), | |
| mean_queue_time_total_per_request = round(mean(qtimes)), | |
| median_queue_time_when_queued = round(as.numeric(median(qtimes[qtimes > 0]))), | |
| median_queue_time_total_per_request = round(as.numeric(median(qtimes)))) | |
| } | |
| get_results = function(router_mode, dyno_count, choice_of_two = FALSE, unicorn_workers_per_dyno = 0, power_of_two = FALSE, parallel_router_count = 1) { | |
| tmp = frac_queued(run_simulation(router_mode = router_mode, dyno_count = dyno_count, choice_of_two = choice_of_two, power_of_two = power_of_two, unicorn_workers_per_dyno = unicorn_workers_per_dyno, reqs_per_minute = 9000, simulation_length_in_minutes = 5, parallel_router_count = parallel_router_count)) | |
| opts = if(choice_of_two) { | |
| "choice of two" | |
| } else if(power_of_two) { | |
| "power_of_two" | |
| } else if(unicorn_workers_per_dyno > 0) { | |
| paste("unicorn", unicorn_workers_per_dyno, "workers per dyno", sep=" ") | |
| } else { | |
| "" | |
| } | |
| tmp$type = paste(router_mode, opts, dyno_count, sep=" ") | |
| tmp$router_type = router_mode | |
| tmp$dyno_count = dyno_count | |
| tmp$router_count = parallel_router_count | |
| return(tmp[, c(6:9, 1:5)]) | |
| } | |
| results = rbind(get_results(router_mode = "intelligent", dyno_count = 15), | |
| get_results(router_mode = "intelligent", dyno_count = 20), | |
| get_results(router_mode = "intelligent", dyno_count = 25), | |
| get_results(router_mode = "naive", dyno_count = 25), | |
| get_results(router_mode = "naive", dyno_count = 25, choice_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 25, power_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 25, unicorn_workers_per_dyno = 2), | |
| get_results(router_mode = "naive", dyno_count = 50), | |
| get_results(router_mode = "naive", dyno_count = 50, choice_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 50, power_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 50, unicorn_workers_per_dyno = 2), | |
| get_results(router_mode = "naive", dyno_count = 100), | |
| get_results(router_mode = "naive", dyno_count = 100, choice_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 100, power_of_two = TRUE), | |
| get_results(router_mode = "naive", dyno_count = 100, unicorn_workers_per_dyno = 2)) | |
| results |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment