Scaleable Computing Jim Gray Microsoft Corporation [email protected] ™ Thesis: Scaleable Servers Scaleable Servers Commodity hardware allows new applications New applications need huge servers Clients and servers are built.
Download ReportTranscript Scaleable Computing Jim Gray Microsoft Corporation [email protected] ™ Thesis: Scaleable Servers Scaleable Servers Commodity hardware allows new applications New applications need huge servers Clients and servers are built.
Scaleable Computing Jim Gray Microsoft Corporation [email protected] ™ Thesis: Scaleable Servers Scaleable Servers Commodity hardware allows new applications New applications need huge servers Clients and servers are built of the same “stuff” Servers should be able to Commodity software and Commodity hardware Scale up (grow node by adding CPUs, disks, networks) Scale out (grow by adding nodes) Scale down (can start small) Key software technologies Objects, Transactions, Clusters, Parallelism 1987: 256 tps Benchmark 14 M$ computer (Tandem) A dozen people False floor, 2 rooms of machines Admin expert Hardware experts A 32 node processor array Simulate 25,600 clients Network expert Manager Performance expert DB expert A 40 GB disk array (80 drives) Auditor OS expert 1988: DB2 + CICS Mainframe 65 tps IBM 4391 Simulated network of 800 clients 2m$ computer Staff of 6 to do benchmark 2 x 3725 network controllers Refrigerator-sized CPU 16 GB disk farm 4 x 8 x .5GB 1997: 10 years later 1 Person and 1 box = 1250 tps 1 Breadbox ~ 5x 1987 machine room 23 GB is hand-held One person does all the work Cost/tps is 1,000x less 25 micro dollars per transaction Hardware expert OS expert Net expert DB expert App expert 4x200 Mhz cpu 1/2 GB DRAM 12 x 4GB disk 3 x7 x 4GB disk arrays What Happened? Moore’s law: Things get 4x better every 3 years (applies to computers, storage, and networks) New Economics: Commodity class price/mips software $/mips k$/year mainframe 10,000 100 minicomputer 100 10 microcomputer 10 1 time GUI: Human - computer tradeoff optimize for people, not computers What Happens Next Last 10 years: 1000x improvement Next 10 years: ???? 1985 1995 2005 Today: text and image servers are free 25 m$/hit => advertising pays for them Future: video, audio, … servers are free “You ain’t seen nothing yet!” Kinds Of Information Processing Point-to-point Immediate Timeshifted Broadcast Conversation Money Lecture Concert Network Mail Book Newspaper Database It’s ALL going electronic Immediate is being stored for analysis (so ALL database) Analysis and automatic processing are being added Low rent min $/byte Shrinks time now or later Shrinks space here or there Automate processing knowbots Immediate OR time-delayed Why Put Everything In Cyberspace? Point-to-point OR broadcast Network Locate Process Analyze Summarize Database Magnetic Storage Cheaper Than Paper File cabinet: cabinet (four drawer) 250$ paper (24,000 sheets) 250$ space (2x3 @ 10$/ft2) 180$ total 700$ 3¢/sheet Disk: Image: disk (4 GB =) ASCII: 2 mil pages 800$ 0.04¢/sheet (80x cheaper) 200,000 pages 0.4¢/sheet Store everything on disk (8x cheaper) Databases Information at Your Fingertips™ Information Network™ Knowledge Navigator™ All information will be in an online database (somewhere) You might record everything you Read: 10MB/day, 400 GB/lifetime (eight tapes today) Hear: 400MB/day, 16 TB/lifetime (three tapes/year today) See: 1MB/s, 40GB/day, 1.6 PB/lifetime (maybe someday) Database Store ALL Data Types The old world: Millions of objects 100-byte objects People Name Address David NY Mike Berk Won Austin The new world: Billions of objects Big objects (1 MB) Objects have behavior (methods) People Name Address Papers David NY Mike Berk Won Austin Picture Voice Paperless office Library of Congress online All information online Entertainment Publishing Business WWW and Internet Billions Of Clients Every device will be “intelligent” Doors, rooms, cars… Computing will be ubiquitous Billions Of Clients Need Millions Of Servers All clients networked to servers May be nomadic or on-demand Fast clients want faster servers Servers provide Shared Data Control Coordination Communication Clients Mobile clients Fixed clients Servers Server Super server Thesis Many little beat few big $1 million 3 1 MM $100 K $10 K Pico Processor Micro Mini Mainframe Nano 1 MB 10 pico-second ram 10 nano-second ram 100 MB 10 GB 10 microsecond ram 1 TB 14" 9" 5.25" 3.5" 2.5" 1.8" 10 millisecond disc 100 TB 10 second tape archive Smoking, hairy golf ball How to connect the many little parts? How to program the many little parts? Fault tolerance? 1 M SPECmarks, 1TFLOP 106 clocks to bulk ram Event-horizon on chip VM reincarnated Multiprogram cache, On-Chip SMP Future Super Server: 4T Machine Array of 1,000 4B machines 1 bps processors 1 BB DRAM 10 BB disks 1 Bbps comm lines 1 TB tape robot A few megabucks Challenge: Manageability Programmability CPU 50 GB Disc 5 GB RAM Cyber Brick a 4B machine Security Availability Scaleability Affordability As easy as a single system Future servers are CLUSTERS of processors, discs Distributed database techniques make clusters work The Hardware Is In Place… And then a miracle occurs ? SNAP: scaleable network and platforms Commodity-distributed OS built on: Commodity platforms Commodity network interconnect Enables parallel applications Thesis: Scaleable Servers Scaleable Servers Commodity hardware allows new applications New applications need huge servers Clients and servers are built of the same “stuff” Servers should be able to Commodity software and Commodity hardware Scale up (grow node by adding CPUs, disks, networks) Scale out (grow by adding nodes) Scale down (can start small) Key software technologies Objects, Transactions, Clusters, Parallelism Scaleable Servers BOTH SMP And Cluster SMP super server Departmental server Personal system Grow up with SMP; 4xP6 is now standard Grow out with cluster Cluster has inexpensive parts Cluster of PCs SMPs Have Advantages Single system image easier to manage, easier to program threads in shared memory, disk, Net 4x SMP is commodity SMP super Software capable of 16x server Problems: Departmental >4 not commodity server Scale-down problem (starter systems expensive) Personal There is a BIGGEST one system Building the Largest Node There is a biggest node (size grows over time) Today, with NT, it is probably 1TB We are building it (with help from DEC and SPIN2) 1 TB GeoSpatial SQL Server database (1.4 TB of disks = 320 drives). 30K BTU, 8 KVA, 1.5 metric tons. 1-TB home page Will put it on the Web as a demo app. 10 meter image of the ENTIRE PLANET. www.SQL.1TB.com 2 meter image of interesting parts (2% of land) Todo loo da loo-rah, ta da ta-la la la Todo loo da loo-rah, ta da ta-la la la Todo loo da loo-rah, ta da ta-la la la One pixel per meter = 500 TB uncompressed. Todo loo da loo-rah, ta da ta-la la la Todo loo da loo-rah, ta da ta-la la la Todo loo da loo-rah, ta da ta-la la la Todo loo da loo-rah, ta da ta-la la la TM Better resolution in US (courtesy of USGS). 1-TB SQL Server DB Satellite and aerial photos Support files What’s TeraByte? 1 Terabyte: 1,000,000,000 business letters 150 miles of book shelf 100,000,000 book pages 15 miles of book shelf 50,000,000 FAX images 7 miles of book shelf 10,000,000 TV pictures (mpeg) 10 days of video 4,000 LandSat images 16 earth images (100m) 100,000,000 web page 10 copies of the web HTML Library of Congress (in ASCII) is 25 TB 1980: $200 million of disc $5 million of tape silo 1997: $200 k$ of magnetic disc $30 k$ nearline tape Terror Byte ! 10,000 discs 10,000 tapes 48 discs 20 tapes TB DB User Interface + + + Next Tpc-C Web-Based Benchmarks Order Invoice Query to server via Web page interface Web server translates to DB SQL does DB work Net: easy to implement performance is GREAT! HTTP Client is a Web browser (7,500 of them!) Submits IIS = Web ODBC SQL Grow UP and OUT 1 Terabyte DB SMP super server Departmental server Personal system Cluster: •a collection of nodes •as easy to program and manage as a single node 1 billion transactions per day Clusters Have Advantages Clients and servers made from the same stuff Inexpensive: Fault tolerance: Spare modules mask failures Modular growth Built with commodity components Grow by adding small modules Unlimited growth: no biggest one Windows NT Clusters Microsoft & 60 vendors defining NT clusters No special hardware needed - but it may help Fault-tolerant first, scaleable second Almost all big hardware and software vendors involved Microsoft, Oracle, SAP giving demos today Enables Commodity fault-tolerance Commodity parallelism (data mining, virtual reality…) Also great for workgroups! Billion Transactions per Day Project Building a 20-node Windows NT Cluster (with help from Intel) > 800 disks All commodity parts Using SQL Server & DTC distributed transactions Each node has 1/20 th of the DB Each node does 1/20 th of the work 15% of the transactions are “distributed” How Much Is 1 Billion Transactions Per Day? 1 Btpd = 11,574 tps (transactions per second) Millions of transactions per day ~ 700,000 tpm 1,000. (transactions/minute) 400 M customers 250,000 ATMs worldwide 7 billion transactions / year (card+cheque) in 1994 0.1 NYSE Visa ~20 M tpd 1. BofA 185 million calls (peak day worldwide) AT&T 10. Visa AT&T Mtpd 100. 1 Btpd Parallelism The OTHER aspect of clusters Clusters of machines allow two kinds of parallelism Many little jobs: online transaction processing TPC-A, B, C… A few big jobs: data search and analysis TPC-D, DSS, OLAP Both give automatic parallelism Kinds of Parallel Execution Pipeline Any Sequential Program Partition outputs split N ways inputs merge M ways Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey Any Sequential Program Any Sequential Program Any Sequential Program Partitioned Execution Spreads computation and IO among processors Count Count Count Count Count Count A Table A...E F...J K...N O...S T...Z Partitioned data gives NATURAL parallelism Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey N x M way Parallelism Merge Merge Merge Sort Sort Sort Sort Sort Join Join Join Join Join A...E F...J K...N O...S T...Z N inputs, M outputs, no bottlenecks. Partitioned Data Partitioned and Pipelined Data Flows Jim Gray & Gordon Bell: VLDB 95 Parallel Database Systems Survey The Parallel Law Of Computing Grosch's Law: 1 MIPS 1$ 2x $ is 4x performance 1,000 MIPS 32 $ .03$/MIPS 2x $ is 2x performance Parallel Law: Needs: Linear speedup and linear scale-up Not always possible 1,000 MIPS 1,000 $ 1 MIPS 1$ Thesis: Scaleable Servers Scaleable Servers Commodity hardware allows new applications New applications need huge servers Clients and servers are built of the same “stuff” Servers should be able to Commodity software and Commodity hardware Scale up (grow node by adding CPUs, disks, networks) Scale out (grow by adding nodes) Scale down (can start small) Key software technologies Objects, Transactions, Clusters, Parallelism The BIG Picture Components and transactions Software modules are objects Object Request Broker (a.k.a., Transaction Processing Monitor) connects objects (clients to servers) Standard interfaces allow software plug-ins Transaction ties execution of a “job” into an atomic unit: all-or-nothing, durable, isolated Object Request Broker Linking And Embedding Objects are data modules; transactions are execution modules Link: pointer to object somewhere else Think URL in Internet Embed: bytes are here Objects may be active; can callback to subscribers Objects Meet Databases The basis for universal data servers, access, & integration object-oriented (COM oriented) programming interface to data Breaks DBMS into components Anything can be a data source Optimization/navigation “on top of” other data sources A way to componentized a DBMS Makes an RDBMS and O-R DBMS (assumes optimizer understands objects) DBMS engine Database Spreadsheet Photos Mail Map Document The Three Tiers Web Client HTML VB Java plug-ins VBscritpt JavaScrpt Middleware VB or Java Script Engine Object server Pool VB or Java Virt Machine Internet HTTP+ DCOM ORB ORB TP Monitor Web Server... Object & Data server. DCOM (oleDB, ODBC,...) IBM Legacy Gateways 43 Server Side Objects Easy Server-Side Execution A Server Give simple execution environment Object gets Network start invoke shutdown Everything else is automatic Drag & Drop Business Objects Queue Connections Context Security Thread Pool Service logic Synchronization Shared Data 47 Configuration Management Receiver A new programming paradigm Develop object on the desktop Better yet: download them from the Net Script work flows as method invocations All on desktop Then, move work flows and objects to server(s) Gives desktop development three-tier deployment Software Cyberbricks Transactions Coordinate Components (ACID) Transaction properties Atomic: all or nothing Consistent: old and new values Isolated: automatic locking or versioning Durable: once committed, effects survive Transactions are built into modern OSs MVS/TM Tandem TMF, VMS DEC-DTM, NT-DTC Transactions & Objects Application requests transaction identifier (XID) XID flows with method invocations Object Managers join (enlist) in transaction Distributed Transaction Manager coordinates commit/abort Distributed Transactions Enable Huge Throughput Each node capable of 7 KtmpC (7,000 active users!) Can add nodes to cluster (to support 100,000 users) Transactions coordinate nodes ORB / TP monitor spreads work among nodes Distributed Transactions Enable Huge DBs Distributed database technology spreads data among nodes Transaction processing technology manages nodes Thesis: Scaleable Servers Scaleable Servers Built from Cyberbricks Servers should be able to Allow new applications Scale up, out, down Key software technologies Clusters (ties the hardware together) Parallelism: (uses the independent cpus, stores, wires Objects (software CyberBricks) Transactions: masks errors.