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Computer Technology Forecast

Computer Technology Forecast. Jim Gray Microsoft Research Gray@Microsoft.com http://~research.Microsoft.com/~Gray. Reality Check. Good news In the limit, processing & storage & network is free Processing & network is infinitely fast Bad news Most of us live in the present.

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Computer Technology Forecast

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  1. ComputerTechnology Forecast Jim Gray Microsoft Research Gray@Microsoft.com http://~research.Microsoft.com/~Gray

  2. Reality Check • Good news • In the limit, processing & storage & network is free • Processing & network is infinitely fast • Bad news • Most of us live in the present. • People are getting more expensive.Management/programming cost exceeds hardware cost. • Speed of light not improving. • WAN prices have not changed much in last 8 years.

  3. Interesting Topics • I’ll talk about server-side hardware • What about client hardware? • Displays, cameras, speech,…. • What about Software? • Databases, data mining, PDB, OODB • Objects / class libraries … • Visualization • Open Source movement

  4. Yotta Zetta Exa Peta Tera Giga Mega Kilo How Much Information Is there? Everything! Recorded • Soon everything can be recorded and indexed • Most data never be seen by humans • Precious Resource: Human attentionAuto-Summarization Auto-Searchis key technology.www.lesk.com/mlesk/ksg97/ksg.html All BooksMultiMedia All LoC books (words) .Movie A Photo A Book 24 Yecto, 21 zepto, 18 atto, 15 femto, 12 pico, 9 nano, 6 micro, 3 milli

  5. Moore’s Law • Performance/Price doubles every 18 months • 100x per decade • Progress in next 18 months = ALL previous progress • New storage = sum of all old storage (ever) • New processing = sum of all old processing. • E. coli double ever 20 minutes! 15 years ago

  6. Trends: ops/s/$ Had Three Growth Phases 1890-1945 Mechanical Relay 7-year doubling 1945-1985 Tube, transistor,.. 2.3 year doubling 1985-2000 Microprocessor 1.0 year doubling

  7. What’s a Balanced System? System Bus PCI Bus PCI Bus

  8. Storage capacity beating Moore’s law • 5 k$/TB today (raw disk)

  9. Cheap Storage • Disks are getting cheap: • 7 k$/TB disks (25 40 GB disks @ 230$ each)

  10. 2x800 Mhz 256 MB Cheap Storage or Balanced System • Low cost storage (2 x 1.5k$ servers) 7K$ TB2x (1K$ system + 8x60GB disks + 100MbEthernet) • Balanced server (7k$/.5 TB) • 2x800Mhz (2k$) • 256 MB (400$) • 8 x 60 GB drives (3K$) • Gbps Ethernet + switch (1.5k$) • 14k$ TB, 28K$/RAIDED TB

  11. The “Absurd” Disk • 2.5 hr scan time (poor sequential access) • 1 aps / 5 GB (VERY cold data) • It’s a tape! 1 TB 100 MB/s 200 Kaps

  12. Hot Swap Drives for Archive or Data Interchange • 25 MBps write(so can write N x 60 GB in 40 minutes) • 60 GB/overnite = ~N x 2 MB/second @ 19.95$/nite 17$ 260$

  13. 240 GB, 2k$ (now)300 GB by year end. • 4x60 GB IDE(2 hot plugable) • (1,100$) • SCSI-IDE bridge • 200k$ • Box • 500 Mhz cpu • 256 MB SRAM • Fan, power, Enet • 700$ • Or 8 disks/box600 GB for ~3K$ ( or 300 GB RAID)

  14. Hot Swap Drives for Archive or Data Interchange • 25 MBps write(so can write N x 74 GB in 3 hours) • 74 GB/overnite = ~N x 2 MB/second @ 19.95$/nite

  15. It’s Hard to Archive a PetabyteIt takes a LONG time to restore it. • At 1GBps it takes 12 days! • Store it in two (or more) places online (on disk?).A geo-plex • Scrub it continuously (look for errors) • On failure, • use other copy until failure repaired, • refresh lost copy from safe copy. • Can organize the two copies differently (e.g.: one by time, one by space)

  16. Disk 60 GB 30 MBps 5 ms seek time 3 ms rotate latency 7$/GB for drive 3$/GB for ctlrs/cabinet 4 TB/rack 1 hour scan Tape 40 GB 10 MBps 10 sec pick time 30-120 second seek time 2$/GB for media8$/GB for drive+library 10 TB/rack 1 week scan Disk vs Tape Guestimates Cern: 200 TB 3480 tapes 2 col = 50GB Rack = 1 TB =20 drives The price advantage of tape is narrowing, and the performance advantage of disk is growing At 10K$/TB, disk is competitive with nearline tape.

  17. Trends: Gilder’s Law: 3x bandwidth/year for 25 more years • Today: • 10 Gbps per channel • 4 channels per fiber: 40 Gbps • 32 fibers/bundle = 1.2 Tbps/bundle • In lab 3 Tbps/fiber (400 x WDM) • In theory 25 Tbps per fiber • 1 Tbps = USA 1996 WAN bisection bandwidth • Aggregate bandwidth doubles every 8 months! 1 fiber = 25 Tbps

  18. Sense of scale 300 MBps OC48 = G2 Or memcpy() • How fat is your pipe? • Fattest pipe on MS campus is the WAN! 20 MBps disk / ATM / OC3 94 MBps Coast to Coast 90 MBps PCI

  19. Redmond/Seattle, WA Information Sciences Institute Microsoft Qwest University of Washington Pacific Northwest Gigapop HSCC (high speed connectivity consortium) DARPA New York Arlington, VA San Francisco, CA 5626 km 10 hops

  20. The Path DC -> SEA C:\tracert -d 131.107.151.194 Tracing route to 131.107.151.194 over a maximum of 30 hops 0 ------- DELL 4400 Win2K WKS Arlington Virginia, ISI Alteon GbE 1 16 ms <10 ms <10 ms 140.173.170.65 ------- Juniper M40 GbE Arlington Virginia, ISI Interface ISIe 2 <10 ms <10 ms <10 ms 205.171.40.61 ------- Cisco GSR OC48 Arlington Virginia, Qwest DC Edge 3 <10 ms <10 ms <10 ms 205.171.24.85 ------- Cisco GSR OC48 Arlington Virginia, Qwest DC Core 4 <10 ms <10 ms 16 ms 205.171.5.233 ------- Cisco GSR OC48 New York, New York, Qwest NYC Core 5 62 ms 63 ms 62 ms 205.171.5.115 ------- Cisco GSR OC48 San Francisco, CA, Qwest SF Core 6 78 ms 78 ms 78 ms 205.171.5.108 ------- Cisco GSR OC48 Seattle, Washington, Qwest Sea Core 7 78 ms 78 ms 94 ms 205.171.26.42 ------- Juniper M40 OC48 Seattle, Washington, Qwest Sea Edge 8 78 ms 79 ms 78 ms 208.46.239.90 ------- Juniper M40 OC48 Seattle, Washington, PNW Gigapop 9 78 ms 78 ms 94 ms 198.48.91.30 ------- Cisco GSR OC48 Redmond Washington, Microsoft 10 78 ms 78 ms 94 ms 131.107.151.194 ------- Compaq SP750 Win2K WKS Redmond Washington, Microsoft SysKonnect GbE

  21. “ PetaBumps” • 751 mbps for 300 seconds = (~28 GB) single-thread single-stream tcp/ip desktop-to-desktop out of the box performance* • 5626 km x 751Mbps = ~ 4.2e15 bit meter / second ~ 4.2 Peta bmps • Multi-steam is 952 mbps~5.2 Peta bmps • 4470 byte MTUs were enabled on all routers. • 20 MB window size

  22. The Promise of SAN/VIA:10x in 2 years http://www.ViArch.org/ • Yesterday: • 10 MBps (100 Mbps Ethernet) • ~20 MBps tcp/ip saturates 2 cpus • round-trip latency ~250 µs • Now • Wires are 10x faster Myrinet, Gbps Ethernet, ServerNet,… • Fast user-level communication • tcp/ip ~ 100 MBps 10% cpu • round-trip latency is 15 us • 1.6 Gbps demoed on a WAN

  23. Pointers • The single-stream submission: http://research.microsoft.com/~gray/papers/Windows2000_I2_land_Speed_Contest_Entry_(Single_Stream_mail).htm • The multi-stream submission: http://research.Microsoft.com/~gray/papers/ Windows2000_I2_land_Speed_Contest_Entry_(Multi_Stream_mail).htm • The code: http://research.Microsoft.com/~gray/papers/speedy.htm speedy.h speedy.cAnd a PowerPoint presentation about it. http://research.Microsoft.com/~gray/papers/ Windows2000_WAN_Speed_Record.ppt

  24. Networking • WANS are getting faster than LANSG8 = OC192 = 8Gbps is “standard” • Link bandwidth improves 4x per 3 years • Speed of light (60 ms round trip in US) • Software stackshave always been the problem. Time = SenderCPU + ReceiverCPU + bytes/bandwidth This has been the problem

  25. Rules of Thumb in Data Engineering • Moore’s law -> an address bit per 18 months. • Storage grows 100x/decade (except 1000x last decade!) • Disk data of 10 years ago now fits in RAM (iso-price). • Device bandwidth grows 10x/decade – so need parallelism • RAM:disk:tape price is 1:10:30 going to 1:10:10 • Amdahl’s speedup law: S/(S+P) • Amdahl’s IO law: bit of IO per instruction/second(tBps/10 top! 50,000 disks/10 teraOP: 100 M$ Dollars) • Amdahl’s memory law: byte per instruction/second (going to 10)(1 TB RAM per TOP: 1 TeraDollars) • PetaOps anyone? • Gilder’s law: aggregate bandwidth doubles every 8 months. • 5 Minute rule: cache disk data that is reused in 5 minutes. • Web rule: cache everything! http://research.Microsoft.com/~gray/papers/MS_TR_99_100_Rules_of_Thumb_in_Data_Engineering.doc

  26. 1,000 x parallel: 100 seconds scan. At 10 MB/s: 1.2 days to scan Use 100 processors & 1,000 disks Dealing With TeraBytes (Petabytes):Requires Parallelism parallelism: use many little devices in parallel

  27. Parallelism Must Be Automatic • There are thousands of MPI programmers. • There are hundreds-of-millions of people using parallel database search. • Parallel programming is HARD! • Find design patterns and automate them. • Data search/mining has parallel design patterns.

  28. Up • “Scale Up” • Use “big iron” (SMP) • Cluster into packs for availability • “Scale Out” clones & partitions • Use commodity servers • Add clones & partitions as needed Out Scalability: Up and Out

  29. Everyone scales outWhat’s the Brick? • 1M$/slice • IBM S390? • Sun E 10,000? • 100 K$/slice • HPUX/AIX/Solaris/IRIX/EMC • 10 K$/slice • Utel / Wintel 4x • 1 K$/slice • Beowulf / Wintel 1x

  30. Farm Partition Clone Pack Shared Nothing Shared Disk Shared Nothing Active-Active Active-Passive Terminology for scaleability • Farms of servers: • Clones: identical • Scaleability + availability • Partitions: • Scaleability • Packs • Partition availability via fail-over • GeoPlex for disaster tolerance.

  31. Farm Partition Clone Pack Shared Nothing Clones Shared Disk Clones Shared Nothing Shared Disk Shared Nothing Partitions Packed Partitions Active-Active Active-Passive

  32. Unpredictable Growth • The TerraServer Story: • We expected 5 M hits per day • We got 50 M hits on day 1 • We peak at 15-20 M hpd on a “hot” day • Average 5 M hpd after 1 year • Most of us cannot predict demand • Must be able to deal with NO demand • Must be able to deal with HUGE demand

  33. An Architecture for Internet Services? • Need to be able to add capacity • New processing • New storage • New networking • Need continuous service • Online change of all components (hardware and software) • Multiple service sites • Multiple network providers • Need great development tools • Change the application several times per year. • Add new services several times per year.

  34. Premise: Each Site is a Farm • Buy computing by the slice (brick): • Rack of servers + disks. • Grow by adding slices • Spread data and computation to new slices • Two styles: • Clones: anonymous servers • Parts+Packs: Partitions fail over within a pack • In both cases, remote farm for disaster recovery

  35. Clones: Availability+Scalability • Some applications are • Read-mostly • Low consistency requirements • Modest storage requirement (less than 1TB) • Examples: • HTML web servers (IP sprayer/sieve + replication) • LDAP servers (replication via gossip) • Replicate app at all nodes (clones) • Spray requests across nodes. • Grow by adding clones • Fault tolerance: stop sending to that clone. • Growth: add a clone.

  36. Shared Nothing Clones Shared Disk Clones Two Clone Geometries • Shared-Nothing: exact replicas • Shared-Disk (state stored in server)

  37. Facilities Clones Need • Automatic replication • Applications (and system software) • Data • Automatic request routing • Spray or sieve • Management: • Who is up? • Update management & propagation • Application monitoring. • Clones are very easy to manage: • Rule of thumb: 100’s of clones per admin

  38. Partitions for Scalability • Clones are not appropriate for some apps. • Statefull apps do not replicate well • high update rates do not replicate well • Examples • Email / chat / … • Databases • Partition state among servers • Scalability (online): • Partition split/merge • Partitioning must be transparent to client.

  39. Partitioned/Clustered Apps • Mail servers • Perfectly partitionable • Business Object Servers • Partition by set of objects. • Parallel Databases • Transparent access to partitioned tables • Parallel Query

  40. Packsfor Availability • Each partition may fail (independent of others) • Partitions migrate to new node via fail-over • Fail-over in seconds • Pack: the nodes supporting a partition • VMS Cluster • Tandem Process Pair • SP2 HACMP • Sysplex™ • WinNT MSCS (wolfpack) • Cluster In A Box now commodity • Partitions typically grow in packs.

  41. What Parts+Packs Need • Automatic partitioning (in dbms, mail, files,…) • Location transparent • Partition split/merge • Grow without limits (100x10TB) • Simple failover model • Partition migration is transparent • MSCS-like model for services • Application-centric request routing • Management: • Who is up? • Automatic partition management (split/merge) • Application monitoring.

  42. Partitions Packed Partitions Partitions and Packs • Packs for availabilty

  43. GeoPlex: Farm pairs • Two farms • Changes from one sent to other • When one farm failsother provides service • Masks • Hardware/Software faults • Operations tasks (reorganize, upgrade move • Environmental faults (power fail)

  44. Services on Clones & Partitions • Application provides a set of services • If cloned: • Services are on subset of clones • If partitioned: • Services run at each partition • System load balancing routes request to • Any clone • Correct partition. • Routes around failures.

  45. Clones for availability Packs for availability Load Balance Web Clients Cluster Scenarios: 3- tier systems A simple web site SQL Database Web File Store SQL Temp State Front End

  46. Packed Partitions: Database Transparency SQL Partition 3 SQL Partition 2 SQL Partition1 SQL Database replication Web File StoreB Cloned Packed file servers The FARM: Clones and Packs of Partitions Cluster Scale Out Scenarios Web File StoreA SQL Temp State ClonedFront Ends(firewall, sprayer, web server) Web Clients Load Balance

  47. Farm Partition Clone Pack Shared Nothing Shared Disk Shared Nothing Active-Active Active-Passive Terminology • Terminology for scaleability • Farms of servers: • Clones: identical • Scaleability + availability • Partitions: • Scaleability • Packs • Partition availability via fail-over • GeoPlex for disaster tolerance.

  48. What we have been doing with SDSS • Helping move the data to SQL • Database design • Data loading • Experimenting with queries on a 4 M object DB • 20 questions like “find gravitational lens candidates” • Queries use parallelism, most run in a few seconds.(auto parallel) • Some run in hours (neighbors within 1 arcsec) • EASY to ask questions. • Helping with an “outreach” website: SkyServer • Personal goal: Try datamining techniques to “re-discover” Astronomy

  49. References (.doc or .pdf) • Technology forecast: http://research.microsoft.com/~gray/papers/MS_TR_99_100_Rules_of_Thumb_in_Data_Engineering.doc • Gbps experiments:http://research.microsoft.com/~gray/ • Disk experiments (10K$ TB)http://research.microsoft.com/~gray/papers/Win2K_IO_MSTR_2000_55.doc • Scaleability Terminologyhttp://research.microsoft.com/~gray/papers/MS_TR_99_85_Scalability_Terminology.doc

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