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# 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
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