Rethinking data centers

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Rethinking data centers
There is nothing more difficult to take in hand,
more perilous to conduct, or more uncertain in
its success, than to take the lead in the
introduction of a new order of things Niccolo
Machiavelli

• Chuck Thacker
• Microsoft Research
• October 2007

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Problems

• Today, we need a new order of things.
• Problem Today, data centers arent designed as
  systems. We need apply system engineering to
• Packaging
• Power distribution
• Computers themselves
• Management
• Networking
• Ill discuss all of these.
• Note Data centers are not mini-Internets

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System design Packaging

• We build large buildings, load them up with lots
  of expensive power and cooling, and only then
  start installing computers.
• Pretty much need to design for the peak load
  (power distribution, cooling, and networking).
• We build them in remote areas.
• Near hydro dams, but not near construction
  workers.
• They must be human-friendly, since we have to
  tinker with them a lot.

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Packaging Another way

• Suns Black Box
• Use a shipping container and build a parking
  lot instead of a building.
• Dont need to be human-friendly.
• Assembled at one location, computers and all. A
  global shipping infrastructure already exists.
• Suns version uses a 20-foot box. 40 might be
  better (Theyve got other problems, too).
• Requires only networking, power, and cooled
  water, so build near a river. Need a pump, but
  no chiller (might have environmental problems).
• Expand as needed, in sensible increments.
• Rackable has a similar system. So (rumored) does
  Google.

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The Black Box
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Suns problems

• Servers are off-the-shelf (with fans).
• They go into the racks sideways, since theyre
  designed for front-to-back airflow.
• Servers have a lot of packaging (later slide).
• Cables exit front and back (mostly back).
• Rack must be extracted to replace a server
• They have a tool, but the procedure is complex.
• Rack as a whole is supported on springs.
• Fixing these problems Later slides.

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Packaging

• A 40 container holds two rows of 16 racks.
• Each rack holds 40 1U servers, plus network
  switch. Total container 1280 servers.
• If each server draws 200W, the rack is 8KW, the
  container is 256 KW.
• A 64-container data center is 16 Mw, plus
  inefficiency and cooling. 82K computers.
• Each container has independent fire suppression.
  Reduces insurance cost.

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System Design Power distribution

• The current picture
• Servers run on 110/220V 60Hz 1F AC.
• Grid -gt Transformer -gt Transfer switch -gt
  Servers.
• Transfer switch connects batteries -gt UPS/ Diesel
  generator.
• Lots and lots of batteries. Batteries require
  safety systems (acid, hydrogen).
• The whole thing nets out at 40 50 efficient,
  gt Large power bills.

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Power Another way

• Servers run on 12V 400 Hz 3F AC.
• Synchronous rectifiers on motherboard make DC
  (with little filtering).
• Rack contains a step-down transformer.
  Distribution voltage is 480 VAC
• Diesel generator supplies backup power.
• Uses a flywheel to store energy until the
  generator comes up.
• When engaged, the frequency and voltage drop
  slightly, but nobody cares.
• Distribution chain
• Grid -gt 60Hz motor -gt 400Hz generator -gt
  Transformer -gt Servers.
• Much more efficient (probably gt 85).
• The Cray 1 (300 KW, 1971) was powered this way.
• Extremely high reliability.

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The Computers

• We currently use commodity servers designed by
  HP, Rackable, others.
• Higher quality and reliability than the average
  PC, but they still operate in the PC ecosystem.
• IBM doesnt.
• Why not roll our own?

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Designing our own

• Minimize SKUs
• One for computes. Lots of CPU, Lots of memory,
  relatively few disks.
• One for storage. Modest CPU, memory, lots of
  disks.
• Like Petabyte?
• Maybe one for DB apps. Properties TBD.
• Maybe Flash memory has a home here.
• What do they look like?

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More on Computers

• Use custom motherboards. We design them,
  optimizing for our needs, not the needs of
  disparate application.
• Use commodity disks.
• All cabling exits at the front of the rack.
• So that its easy to extract the server from the
  rack.
• Use no power supplies other than on-board
  inverters (we have these now).
• Error correct everything possible, and check what
  we cant correct.
• When a component doesnt work to its spec, fix it
  or get another, rather than just turning the
  feature off.
• For the storage SKU, dont necessarily use
  Intel/AMD processors.
• Although both seem to be beginning to understand
  that low power/energy efficiency is important.

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Management

• Were doing pretty well here.
• Automatic management is getting some traction,
  opex isnt overwhelming.
• Could probably do better
• Better management of servers to provide more
  fine-grained control than just reboot, reimage.
• Better sensors to diagnose problems, predict
  failures.
• Measuring airflow is very important (not just
  finding out whether the fan blades turn, since
  there are no fans in the server).
• Measure more temperatures than just the CPU.
• Modeling to predict failures?
• Machine learning is your friend.

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Networking

• A large part of the total cost.
• Large routers are very expensive, and command
  astronomical margins.
• They are relatively unreliable we sometimes see
  correlated failures in replicated units.
• Router software is large, old, and
  incomprehensible frequently the cause of
  problems.
• Serviced by the manufacturer, and theyre never
  on site.
• By designing our own, we save money and improve
  reliability.
• We also can get exactly what we want, rather than
  paying for a lot of features we dont need (data
  centers arent mini-Internets).

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Data center network differences

• We know (and define) the topology
• The number of nodes is limited
• Broadcast/multicast is not required.
• Security is simpler.
• No malicious attacks, just buggy software and
  misconfiguration.
• We can distribute applications to servers to
  distribute load and minimize hot spots.

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Data center network design goals

• Reduce the need for large switches in the core.
• Simplify the software. Push complexity to the
  edge of the network.
• Improve reliability
• Reduce capital and operating cost.

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One approach to data center networks

• Use a 64-node destination-addressed hypercube at
  the core.
• In the containers, use source routed trees.
• Use standard link technology, but dont need
  standard protocols.
• Mix of optical and copper cables. Short runs are
  copper, long runs are optical.

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Basic switches

• Type 1 Forty 1 Gb/sec ports, plus 2 10 Gb/sec
  ports.
• These are the leaf switches. Center uses 2048
  switches, one per rack.
• Not replicated (if a switch fails, lose 1/2048 of
  total capacity)
• Type 2 Ten 10 Gb/sec ports
• These are the interior switches
• There are two variants, one for destination
  routing, one for tree routing. Hardware is
  identical, only FPGA bit streams differ.
• Hypercube uses 64 switches, containers use 10
  each. 704 switches total.
• Both implemented with Xilinx Virtex 5 FPGAs.
• Type 1 also uses Gb Ethernet Phy chips
• These contain 10 Gb Phys, plus buffer memories
  and logic.
• We can prototype both types with BEE3.
• Real switches use less expensive FPGAs

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Data center network
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Hypercube properties

• Minimum hop count
• Even load distribution for all-all communication.
• Reasonable bisection bandwidth.
• Can route around switch/link failures.
• Simple routing
• Outport f(Dest xor NodeNum)
• No routing tables
• Switches are identical.
• Use destination based input buffering, since a
  given link carries packets for a limited number
  of destinations.
• Linkbylink flow control to eliminate drops.
• Links are short enough that this doesnt cause a
  buffering problem.

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A 16-node (dimension 4) hypercube
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Routing within a container
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Bandwidths

• Servers gt network 82 Tb/sec
• Containers gt cube 2.5 Tb/sec
• Average will be lower ?.

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Objections to this scheme

• Commodity hardware is cheaper
• This is commodity hardware. Even for one center
  (and we build many).
• And in the case of the network, its not cheaper.
  Large switches command very large margins.
• Standards are better
• Yes, but only if they do what you need, at
  acceptable cost.
• It requires too many different skills
• Not as many as you might think.
• And we would work with engineering/manufacturing
  partners who would be the ultimate manufacturers.
  This model has worked before.
• If this stuff is so great, why arent others
  doing it?
• They are.

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Conclusion

• By treating data centers as systems, and doing
  full-system optimization, we can achieve
• Lower cost, both opex and capex.
• Higher reliability.
• Incremental scale-out.
• More rapid innovation as technology improves.

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Questions?