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BARC

Explore the benefits of telepresence, including space shifting, reduce travel, time shifting, and just-in-time meetings. Discover how telework and telepresentations can improve team interactions and productivity. Learn about scalable reliable multicast technologies such as SRM and ECSRM. See prototypes like PowerCast and FileCast that demonstrate the power of telepresence. Discover RAGS, a random SQL test generator, and how it is used in SQL 7.0 testing.

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BARC

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  1. BARC BARCMicrosoft Bay Area Research CenterTom Barclay Gordon Bell Joe Barrera Jim Gemmell Jim Gray Erik Riedel (CMU) Eve Schooler (Cal Tech) Don Slutz Catherine Van Ingenhttp://www.research.Microsoft.com/barc/

  2. Telepresence • The next killer app • Space shifting: • Reduce travel • Time shifting: • Retrospective • Offer condensations • Just in time meetings. • Example: ACM 97 • NetShow and Web site. • More web visitors than attendees • People-to-People communication

  3. Working with NorCalAn Experiment in Presence Is being there, then better than being somewhere else at some other time? December 11, 1997

  4. Telework = work + telepresence “being there while being here” • The teleworkplace is just an office with limited • Communication, computer, and network support! • Team interactions for work! Until we understand in situ collaboration, CSCW is a “rat hole”! • Serendipitous social interaction in hallway, office, coffee place, meeting room, etc. • Administrative support for helping, filing, sending, etc. • Telepresentations and communication • Computing environment …being always connected and operational, administrivia, help in managing phones and messages, information (especially paper) management • SOHOs & COMOHOs is a high growth market

  5. IP Multicast • Is pruned broadcast to a multicast address • Unreliable • Reliable would require Ack/Nack. • State or Nack implosion problem router router router router =sender =receiver =not interested

  6. What We Are Doing • Scalable Reliable Multicast (SRM) • used by WB (white board) of Mbone • Nack suppression (backoff) • N2message traffic to set up • Error Correcting SRM (EC SRM) • Do not resend lost packets. • Send Error Correction in addition to regular • (or)Send Error Correction in response to NACK • One EC packet repairs any of k lost packets • Improved scaleability (millions of subscribers).

  7. Encode (copy 1st k) 1 2 k Take any k 1 2 k k+1 k+2 n Decode Original packets 1 2 k (n,k) encoding Original packets

  8. X 1234567 1234567 1234567 1234567 1234567 1234567 1234567 X X X X X X ECSRM • Combine suppression & erasure correction • Assign each packet to an EC group of size k • NACK: (group, # missing) • NACK of (g,c) suppresses all (g,xc). • Don’t re-send originals; send EC packets using (n,k) encoding • Below, 1 NACK and one EC packet fixes all errors. 1234567 EC

  9. Telepresence Prototypes • PowerCast: multicast PowerPoint • Streaming - pre-sends next anticipated slide • Send slides and voice rather than talking head and voice • Uses ECSRM for reliable multicast • 1000’s of receivers can join and leave any time. • No server needed; no pre-load of slides. • Cooperating with NetShow • FileCast: multicast file transfer. • Erasure encodes all packets • Receivers only need to receive as many bytes as the length of the file • Multicast IE to solve Midnight-Madness problem • NT SRM: reliable IP multicast library for NT

  10. RAGS: RAndom SQL test Generator • Microsoft spends a LOT of money on testing. • Idea: test SQL by • generating random correct queries • executing queries against database • compare results with SQL 6.5, DB2, Oracle • Being used in SQL 7.0 testing. • 185 unique bugs found (since 2/97) • Very productive test tool

  11. SELECT TOP 3 T1.royalty , T0.price , "Apr 15 1996 10:23AM" , T0.notes FROM titles T0, roysched T1 WHERE EXISTS ( SELECT DISTINCT TOP 9 $3.11 , "Apr 15 1996 10:23AM" , T0.advance , ( "<v3``VF;" +(( UPPER(((T2.ord_num +"22\}0G3" )+T2.ord_num ))+("{1FL6t15m" + RTRIM( UPPER((T1.title_id +((("MlV=Cf1kA" +"GS?" )+T2.payterms )+T2.payterms ))))))+(T2.ord_num +RTRIM((LTRIM((T2.title_id +T2.stor_id ))+"2" ))))), T0.advance , (((-(T2.qty ))/(1.0 ))+(((-(-(-1 )))+( DEGREES(T2.qty )))-(-(( -4 )-(-(T2.qty ))))))+(-(-1 )) FROM sales T2 WHERE EXISTS ( SELECT "fQDs" , T2.ord_date , AVG ((-(7 ))/(1 )), MAX (DISTINCT -1 ), LTRIM("0I=L601]H" ), ("jQ\" +((( MAX(T3.phone )+ MAX((RTRIM( UPPER( T5.stor_name ))+((("<" +"9n0yN" )+ UPPER("c" ))+T3.zip ))))+T2.payterms )+ MAX("\?" ))) FROM authors T3, roysched T4, stores T5 WHERE EXISTS ( SELECT DISTINCT TOP 5 LTRIM(T6.state ) FROM stores T6 WHERE ( (-(-(5 )))>= T4.royalty ) AND (( ( ( LOWER( UPPER((("9W8W>kOa" + T6.stor_address )+"{P~" ))))!= ANY ( SELECT TOP 2 LOWER(( UPPER("B9{WIX" )+"J" )) FROM roysched T7 WHERE ( EXISTS ( SELECT (T8.city +(T9.pub_id +((">" +T10.country )+ UPPER( LOWER(T10.city))))), T7.lorange , ((T7.lorange )*((T7.lorange )%(-2 )))/((-5 )-(-2.0 )) FROM publishers T8, pub_info T9, publishers T10 WHERE ( (-10 )<= POWER((T7.royalty )/(T7.lorange ),1)) AND (-1.0 BETWEEN (-9.0 ) AND (POWER(-9.0 ,0.0)) ) ) --EOQ ) AND (NOT (EXISTS ( SELECT MIN (T9.i3 ) FROM roysched T8, d2 T9, stores T10 WHERE ( (T10.city + LOWER(T10.stor_id )) BETWEEN (("QNu@WI" +T10.stor_id )) AND ("DT" ) ) AND ("R|J|" BETWEEN ( LOWER(T10.zip )) AND (LTRIM( UPPER(LTRIM( LOWER(("_\tk`d" +T8.title_id )))))) ) GROUP BY T9.i3, T8.royalty, T9.i3 HAVING -1.0 BETWEEN (SUM (-( SIGN(-(T8.royalty ))))) AND (COUNT(*)) ) --EOQ ) ) ) --EOQ ) AND (((("i|Uv=" +T6.stor_id )+T6.state )+T6.city ) BETWEEN ((((T6.zip +( UPPER(("ec4L}rP^<" +((LTRIM(T6.stor_name )+"fax<" )+("5adWhS" +T6.zip )))) +T6.city ))+"" )+"?>_0:Wi" )) AND (T6.zip ) ) ) AND (T4.lorange BETWEEN ( 3 ) AND (-(8 )) ) ) ) --EOQ GROUP BY ( LOWER(((T3.address +T5.stor_address )+REVERSE((T5.stor_id +LTRIM( T5.stor_address )))))+ LOWER((((";z^~tO5I" +"" )+("X3FN=" +(REVERSE((RTRIM( LTRIM((("kwU" +"wyn_S@y" )+(REVERSE(( UPPER(LTRIM("u2C[" ))+T4.title_id ))+( RTRIM(("s" +"1X" ))+ UPPER((REVERSE(T3.address )+T5.stor_name )))))))+ "6CRtdD" ))+"j?]=k" )))+T3.phone ))), T5.city, T5.stor_address ) --EOQ ORDER BY 1, 6, 5 ) Sample Rags Generated Statement This Statement yields an error:SQLState=37000, Error=8623Internal Query Processor Error:Query processor could not produce a query plan.

  12. Reduced Statement Causes Same Error SELECT roysched.royalty FROM titles, roysched WHERE EXISTS ( SELECT DISTINCT TOP 1 titles.advance FROM sales ORDER BY 1) • Next steps: • Auto-Simplify failure cases • Compare outputs with other products • Extend to other parts of SQL • Patents

  13. Scaleup - Big Database • Build a 1 TB SQL Server database • Show off Windows NT and SQL Server scalability • Stress test the product • Data must be • 1 TB • Unencumbered • Interesting to everyone everywhere • And not offensive to anyone anywhere • Loaded • 1.1 M place names from Encarta World Atlas • 1 M Sq Km from USGS (1 meter resolution) • 2 M Sq Km from Russian Space agency (2 m) • Will be on web (world’s largest atlas) • Sell images with commerce server. • USGS CRDA: 3 TB more coming.

  14. SPIN-2 The System • DEC Alpha + 8400 • 324 StorageWorks Drives (2.8 TB) • SQL Server 7.0 • USGS 1-meter data (30% of US) • Russian Space data1.6 meterresolutionimages

  15. Demo Http://t2b2c

  16. Technical ChallengeKey idea • Problem: Geo-Spatial Search without geo-spatial access methods.(just standard SQL Server) • Solution: • Geo-spatial search key: • Divide earth into rectangles of 1/48th degree longitude (X) by 1/96th degree latitude (Y) • Z-transform X & Y into single Z value, build B-tree on Z • Adjacent images stored next to each other • Search Method: • Latitude and Longitude => X, Y, then Z • Select on matching Z value

  17. New Since S-Day: More data: 4.8 TB USGS DOQ .5 TB Russian Bigger Server: Alpha 8400 8 proc, 8 GB RAM, 2.8 TB Disk Improved Application Better UI Uses ASP Commerce App Cut images and Load Dec&Jan Built Commerce App for USGS & Spin-2 Release on Internet with Sphinx B2 Launch on Internet in Spring Live on the internet in 98H1(Tied to Sphinx Beta 2 RTM )For 18 Months

  18. NT Clusters (Wolfpack) • Scale DOWN to PDA: WindowsCE • Scale UP an SMP: TerraServer • Scale OUT with a cluster of machines • Single-system image • Naming • Protection/security • Management/load balance • Fault tolerance • “Wolfpack” • Hot pluggable hardware & software

  19. Browser Server 1 Web site Database Symmetric Virtual Server Failover Example Server 1 Server 2 Web site Web site Database Database Web site files Web site files Database files Database files

  20. Clusters & BackOffice • Research: Instant & Transparent failover • Making BackOffice PlugNPlay on Wolfpack • Automatic install & configure • Virtual Server concept makes it easy • simpler management concept • simpler context/state migration • transparent to applications • SQL 6.5E & 7.0 Failover • MSMQ (queues), MTS (transactions).

  21. 1.2 B tpd • 1 B tpd ran for 24 hrs. • Out-of-the-box software • Off-the-shelf hardware • AMAZING! • Sized for 30 days • Linear growth • 5 micro-dollars per transaction

  22. The Memory Hierarchy • Measuring & Modeling Sequential IO • Where is the bottleneck? • How does it scale with • SMP, RAID, new interconnects Goals: balanced bottlenecks Low overhead Scale many processors (10s) Scale many disks (100s) Memory App address space Mem bus File cache Controller Adapter SCSI PCI

  23. System Bus 422 MBps 40 MBps 7.2 MB/s 7.2 MB/s Application 10-15 MBps Data 7.2 MB/s File System SCSI Buffers Disk 133 MBps PCI 7.2 MB/s PAP (peak advertised Performance) vsRAP (real application performance) • Goal: PAP = RAP / 2 (the half-power point)

  24. Temp file Read / Write File System Cache Program uses small (in cpu cache) buffer. So, write/read time is bus move time (3x better than copy) Paradox: fastest way to move data is to write then read it. This hardware is limited to 150 MBps per processor The Best Case: Temp File, NO IO

  25. 64KB Out of the Box Disk File Performance • One NTFS disk • Buffered read • NTFS does 64 KB read-ahead • if you ask FILE_FLAG_SEQUENTIAL • or if it thinks you are sequential • NTFS does 64 KB write behind • under same conditions • aggregates many small IO to few big IO.

  26. Read throughput is GREAT! Write throughput is 40% of read WCE is fast but dangerous Net: default out of the box performance is good. 20 ms/MB ~ 2 instructions/byte! CPU will saturate at 50MBps Synchronous Buffered Read/Write

  27. Bottleneck Analysis • Drawn to linear scale Theoretical Bus Bandwidth 422MBps = 66 Mhz x 64 bits MemoryRead/Write ~150 MBps MemCopy ~50 MBps Disk R/W ~9MBps

  28. Parallel Access To Data? At 10 MB/s 1.2 days to scan 1,000 x parallel 100 second SCAN. 1 Terabyte 1 Terabyte BANDWIDTH 10 GB/s 10 MB/s Parallelism: divide a big problem into many smaller ones to be solved in parallel.

  29. Reads are easy, writes are hard Async write can match WCE. 422 MBps 142 MBps SCSI Disks Application Data 40 MBps 10-15 MBps 31 MBps File System 9 MBps 133 MBps SCSI 72 MBps PCI PAP vs RAP

  30. Bottleneck Analysis • NTFS Read/Write 9 disk, 2 SCSI bus, 1 PCI ~ 65 MBps Unbuffered read ~ 43 MBps Unbuffered write ~ 40 MBps Buffered read ~ 35 MBps Buffered write Adapter ~30 MBps Memory Read/Write ~150 MBps PCI ~70 MBps 70 MBps Adapter

  31. Some servers absorb memory circumvent NT memory management. Complicated by Wolfpack failover, large memory support, interactions among servers. Prototype Memory Broker service augments NT memory management: Separates memory needs and desires Dynamic expand & reclaim memory footprint Monitor memory usage, paging, shared buffers Cross-server arbitration Clients: SQL Server, Exchange, Oracle,.. Working with NT-Team (Lou Perazzoli) NT Memory Broker

  32. Public Service • Gordon Bell • Computer Museum • Vanguard Group • Edits column in CACM • Jim Gray • National Research Council Computer Science and Telecommunications Board • Presidential Advisory Committee on NGI-IT-HPPC • Edit Journals & Conferences. • Tom Barclay • USGS and Russian cooperative research

  33. BARCMicrosoft Bay Area Research CenterTom Barclay Gordon Bell Joe Barrera Jim Gemmell Jim Gray Don Slutz Catherine Van Ingenhttp://www.research.Microsoft.com/barc/

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