I'm sure this is covered well elsewhere online, but that's never the point of
these things ;)
I was helping with some capacity planning and run-rate math today at work and
found that ec2 reserved instances are much cheaper compared to on-demand - If
this is obvious to you, chill out, I have historically never really used EC2
nor have I ever been close to budgeting. ;)
I proved this conclusion with some math, but frankly I like visualizations
better, so I decided to learn R. I wrote an R script that will graph an
on-demand vs reserved pricing for one m1.large instance (code at end of the
post).
The result is this graph:
The graph says it all, and definitely tells me that we need to be reserving all
of our instances at Loggly - and it gives me a rule-of-thumb:
- If we're going to use one instance unit for at least 9 months, reserve for 3 years.
- If we're going to use one instance unit for at least 6 months, reserve for 1 year.
- Otherwise, stick with on-demand.
The "reserved instances" pay structure is you pay a one-time fee for access to
a reduced hourly rate.
This also means that our random "debug something" deployments that are shutdown
much of the time are probably best off being reserved instances as well- at
least for a 1-year thing - since we are likely to use those deployments for
more than half of a year.
A 3-year on-demand price for m1.large is just shy of $9000, which is twice
as expensive as the 3-year reserve. Capaticy plan and maybe start buying
reserved instances. Make your CFO happy.
And in case you were going to ask, I ran the same plot with data from EC2
"quaduple extra large" instances and the savings and break-even points were the
same. I bet the rest of the prices flow similarly.
The R script is follows, run it with 'R --save < yourscript.r':
# Values taken from http://aws.amazon.com/ec2/pricing/
# for an m1.large ("Large") instance
on_demand_hourly = 0.34
reserve_hourly = 0.12
reserve_1year = 910
reserve_3year = 1400
# quadruple extra large instances
#on_demand_hourly = 1.60
#reserve_hourly = 0.56
#reserve_1year = 4290
#reserve_3year = 6590
on_demand_daily = on_demand_hourly * 24
reserve_daily = reserve_hourly * 24
x <- c(0, 365)
y <- on_demand_daily * x
# Calculate day of break-even point reserve vs on-demand rates
break_1year_x = reserve_1year / (on_demand_daily - reserve_daily)
break_3year_x = reserve_3year / (on_demand_daily - reserve_daily)
png(filename = "ec2_m1large_cost.png", width = 500, height=375)
plot(x,y, type="l", col='red', xlab="", ylab="cost ($USD)")
title("EC2 cost analysis for m1.large",
sprintf("(days)\n1-year is cheaper than on-demand after %.0f days of usage,\n 3-year is cheaper after %.0f days", break_1year_x, break_3year_x))
text(60, 0, sprintf("on-demand=$%.2f/hour", on_demand_hourly), pos=3)
abline(reserve_1year, reserve_daily, col='green')
text(60, reserve_1year, sprintf("1-year=$%.0f+$%.2f/hour", reserve_1year, reserve_hourly), pos=3)
abline(reserve_3year, reserve_daily, col='blue')
text(60, reserve_3year, sprintf("3-year=$%.0f+$%.2f/hour", reserve_3year, reserve_hourly), pos=3)
point_y = reserve_1year + reserve_daily * break_1year_x
points(break_1year_x, point_y)
text(break_1year_x, point_y, labels = sprintf("%.0f days", break_1year_x), pos=1)
point_y = reserve_3year + reserve_daily * break_3year_x
points(break_3year_x, point_y)
text(break_3year_x, point_y, labels = sprintf("%.0f days", break_3year_x), pos=1)
dev.off()
quit()
