3 ms·
The author handwaves away the power savings of powering down disks by talking about the capital cost of providing power. "Unless the prices of copper, PDUs and
by reitzensteinm 13y ago
The author handwaves away the power savings of powering down disks by talking about the capital cost of providing power.
"Unless the prices of copper, PDUs and diesel-generators have started following Moore’s Law,
this is probably more true today than in 2007."
But this is fallaciously assuming that Glacier servers would need to be like EC2 or S3 servers that are switched on for x random hours per day. This isn't the case at all.
Diesel generators, for instance - what's the use? If you're replicating the data around the world, it doesn't matter if your Glacier servers in one location are powered down for days.
Cooling, power - just switch off every single Glacier server at each location during the peak hours of the day. No additional capital costs, because you're never adding to peak power usage.
Distribution of reads - Profile customers based on how frequently they're reading from the data store. I bet 50% of customers never read a single byte back. Colocate these customers on the same servers, and you only have to power up the server once a month to check if the data is still there.
Bonus - Profile data access patterns with simple heuristics to determine what's likely to be read back in, and temporarily store that data in S3. Imagine a company that archives everything to Glacier daily, but restores day, week and month old backups regularly. Keep all data less than a month old in S3, and the rest on the indefinitely powered down servers talked about above.
I actually quite liked the author's case for BDXL, but it seems he's doing a straw man on other possible solutions. Well implemented BDXL more cost efficient than a naively implemented disk strategy? Not exactly news.
- raverbashing 13y agoMakes me wonder something Database machines are usually never shut down. And when they are, it's usually a manual process (unless we're talking about a power failure or something) "Diesel generators, for instance - what's the use?" Backup power (not only for Glacier). Reducing power consumption in peak hours is a good strategy, however, I'm not sure how worried Amazon is about this (and the difference in price between regular hours and peak hours)
- reitzensteinm 13y agoIt's not about the price they're paying for power (peak hours from the power company's perspective), it's about the peak usage of their data center. As the article (correctly) says, the capital costs are dependent on your peak usage. If you have 1000 servers using 400kW at peak, you need sufficient air conditioning to extract 400kW worth of heat, and backup generation capable of producing 400kW. It doesn't matter if you only use 100kW 16 hours a day - the capital costs are the same. I'm suggesting that Glacier could live entirely in non peak periods, meaning that the capital costs are unchanged and the demand curve is flattened.
- raverbashing 13y agoThe real question then is: is there really a peak hour for datacenter usage/consumption? What's the consumption difference between peak and regular hours (and low-demand hours) How much power does Amazon use during mornings compared to Netflix watching peak time?
- keypusher 13y agoYes, I'm sure there is. Look at Google clicks by hour or similar metrics, there is a significant curve during the day, peak can be 4-5x the low.
- jpalomaki 13y agoI think the requirements for power are much different in case of glacier that with some more generic computing. With a service like glacier, running on hard disks, you could probably completely turn off large parts of your infrastructure. With 5 hour read latency there should be plenty of opportunities to optimize the reads so that only a small part of the infra needs to be powered on at any time. Also you might be able to skip the backup power part completely. The data is anyways distributed to few locations. The probability of many of them losing to power at the same time for several hours is likely to be very small. Maybe there's also large differences in how the customers use the system. I would assume some customers are more likely to retrieve data than others. Once you start learning these patterns you could further optimize the storage. The pricing of Glacier is not necessarily driven by technical reasons. Amazon already has S3 and they need to differentiate the products.