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ISTORE-1 Update

ISTORE-1 Update. David Patterson University of California at Berkeley Patterson@cs.berkeley.edu UC Berkeley IRAM Group UC Berkeley ISTORE Group istore-group@cs.berkeley.edu July 2000. Perspective on Post-PC Era. PostPC Era will be driven by 2 technologies:

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ISTORE-1 Update

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  1. ISTORE-1 Update David Patterson University of California at Berkeley Patterson@cs.berkeley.edu UC Berkeley IRAM Group UC Berkeley ISTORE Group istore-group@cs.berkeley.edu July 2000

  2. Perspective on Post-PC Era • PostPC Era will be driven by 2 technologies: 1) “Gadgets”:Tiny Embedded or Mobile Devices • ubiquitous: in everything • e.g., successor to PDA, cell phone, wearable computers 2) Infrastructure to Support such Devices • e.g., successor to Big Fat Web Servers, Database Servers

  3. Outline • Motivation for the ISTORE project • AME: Availability, Maintainability, Evolutionary growth • ISTORE’s research principles & techniques • Introspection • SON: Storage-Oriented Node In Cluster • RAIN: Redundant Array of Inexpensive Network switches • Benchmarks for AME • A Case for SON vs. CPUs • Applications, near term and future • Conclusions and future work

  4. Lampson: Systems Challenges • Systems that work • Meeting their specs • Always available • Adapting to changing environment • Evolving while they run • Made from unreliable components • Growing without practical limit • Credible simulations or analysis • Writing good specs • Testing • Performance • Understanding when it doesn’t matter “Computer Systems Research-Past and Future” Keynote address, 17th SOSP, Dec. 1999 Butler Lampson Microsoft

  5. Hennessy: What Should the “New World” Focus Be? • Availability • Both appliance & service • Maintainability • Two functions: • Enhancing availability by preventing failure • Ease of SW and HW upgrades • Scalability • Especially of service • Cost • per device and per service transaction • Performance • Remains important, but its not SPECint “Back to the Future: Time to Return to Longstanding Problems in Computer Systems?” Keynote address, FCRC, May 1999 John Hennessy Stanford

  6. The real scalability problems: AME • Availability • systems should continue to meet quality of service goals despite hardware and software failures • Maintainability • systems should require only minimal ongoing human administration, regardless of scale or complexity: Today, cost of maintenance = 10X cost of purchase • Evolutionary Growth • systems should evolve gracefully in terms of performance, maintainability, and availability as they are grown/upgraded/expanded • These are problems at today’s scales, and will only get worse as systems grow

  7. Principles for achieving AME (1) • No single points of failure • Redundancy everywhere • Performance robustness is more important than peak performance • “performance robustness” implies that real-world performance is comparable to best-case performance • Performance can be sacrificed for improvements in AME • resources should be dedicated to AME • compare: biological systems spend > 50% of resources on maintenance • can make up performance by scaling system

  8. Principles for achieving AME (2) • Introspection • reactive techniques to detect and adapt to failures, workload variations, and system evolution • proactive techniques to anticipate and avert problems before they happen

  9. CPU Hardware Techniques (1): SON • SON: Storage Oriented Nodes (in clusters) • Distribute processing with storage • If AME really important, provide resources! • Most storage servers limited by speed of CPUs!! • Amortize sheet metal, power, cooling, network for disk to add processor, memory, and a real network? • Embedded processors 2/3 perf, 1/10 cost, power? • Serial lines, switches also growing with Moore’s Law; less need today to centralize vs. bus oriented systems • Advantages of cluster organization • Truly scalable architecture • Architecture that tolerates partial failure • Automatic hardware redundancy

  10. Hardware techniques (2) • Heavily instrumented hardware • sensors for temp, vibration, humidity, power, intrusion • helps detect environmental problems before they can affect system integrity • Independent diagnostic processor on each node • provides remote control of power, remote console access to the node, selection of node boot code • collects, stores, processes environmental data for abnormalities • non-volatile “flight recorder” functionality • all diagnostic processors connected via independent diagnostic network

  11. Hardware techniques (3) • On-demand network partitioning/isolation • Internet applications must remain available despite failures of components, therefore can isolate a subset for preventative maintenance • Allows testing, repair of online system • Managed by diagnostic processor and network switches via diagnostic network

  12. Hardware techniques (4) • Built-in fault injection capabilities • Power control to individual node components • Injectable glitches into I/O and memory busses • Managed by diagnostic processor • Used for proactive hardware introspection • automated detection of flaky components • controlled testing of error-recovery mechanisms • Important for AME benchmarking (see next slide)

  13. Disk Half-height canister ISTORE-1 hardware platform • 80-node x86-based cluster, 1.4TB storage • cluster nodes are plug-and-play, intelligent, network-attached storage “bricks” • a single field-replaceable unit to simplify maintenance • each node is a full x86 PC w/256MB DRAM, 18GB disk • more CPU than NAS; fewer disks/node than cluster Intelligent Disk “Brick” Portable PC CPU: Pentium II/266 + DRAM Redundant NICs (4 100 Mb/s links) Diagnostic Processor • ISTORE Chassis • 80 nodes, 8 per tray • 2 levels of switches • 20 100 Mbit/s • 2 1 Gbit/s • Environment Monitoring: • UPS, redundant PS, • fans, heat and vibration sensors...

  14. ISTORE-1 Status • 10 Nodes manufactured; 60 board fabbed, 25 to go • Boots OS • Diagnostic Processor Interface SW complete • PCB backplane: not yet designed • Finish 80 node system: Summer 2000

  15. A glimpse into the future? • System-on-a-chip enables computer, memory, redundant network interfaces without significantly increasing size of disk • ISTORE HW in 5-7 years: • building block: 2006 MicroDrive integrated with IRAM • 9GB disk, 50 MB/sec from disk • connected via crossbar switch • If low power, 10,000 nodes fit into one rack! • O(10,000) scale is our ultimate design point

  16. Hardware Technique (6): RAIN • Switches for ISTORE-1 substantial fraction of space, power, cost, and just 80 nodes! • Redundant Array of Inexpensive Disks (RAID): replace large, expensive disks by many small, inexpensive disks, saving volume, power, cost • Redundant Array of Inexpensive Network switches: replace large, expensive switches by many small, inexpensive switches, saving volume, power, cost? • ISTORE-1: Replace 2 16-port 1-Gbit switches by fat tree of 8 8-port switches, or 24 4-port switches?

  17. “Hardware” techniques (6) • Benchmarking • One reason for 1000X processor performance was ability to measure (vs. debate) which is better • e.g., Which most important to improve: clock rate, clocks per instruction, or instructions executed? • Need AME benchmarks “what gets measured gets done” “benchmarks shape a field” “quantification brings rigor”

  18. Availability benchmark methodology • Goal: quantify variation in QoS metrics as events occur that affect system availability • Leverage existing performance benchmarks • to generate fair workloads • to measure & trace quality of service metrics • Use fault injection to compromise system • hardware faults (disk, memory, network, power) • software faults (corrupt input, driver error returns) • maintenance events (repairs, SW/HW upgrades) • Examine single-fault and multi-fault workloads • the availability analogues of performance micro- and macro-benchmarks

  19. Benchmark Availability?Methodology for reporting results • Results are most accessible graphically • plot change in QoS metrics over time • compare to “normal” behavior? • 99% confidence intervals calculated from no-fault runs

  20. Example single-fault result • Compares Linux and Solaris reconstruction • Linux: minimal performance impact but longer window of vulnerability to second fault • Solaris: large perf. impact but restores redundancy fast Linux Solaris

  21. Software techniques • Fully-distributed, shared-nothing code • centralization breaks as systems scale up O(10000) • avoids single-point-of-failure front ends • Redundant data storage • required for high availability, simplifies self-testing • replication at the level of application objects • application can control consistency policy • more opportunity for data placement optimization

  22. Software techniques (2) • “River” storage interfaces • NOW Sort experience: performance heterogeneity is the norm • e.g., disks: outer vs. inner track (1.5X), fragmentation • e.g., processors: load (1.5-5x) • So demand-driven delivery of data to apps • via distributed queues and graduated declustering • for apps that can handle unordered data delivery • Automatically adapts to variations in performance of producers and consumers • Also helps with evolutionary growth of cluster

  23. Software techniques (3) • Reactive introspection • Use statistical techniques to identify normal behavior and detect deviations from it • Policy-driven automatic adaptation to abnormal behavior once detected • initially, rely on human administrator to specify policy • eventually, system learns to solve problems on its own by experimenting on isolated subsets of the nodes • one candidate: reinforcement learning

  24. Software techniques (4) • Proactive introspection • Continuous online self-testing of HW and SW • in deployed systems! • goal is to shake out “Heisenbugs” before they’re encountered in normal operation • needs data redundancy, node isolation, fault injection • Techniques: • fault injection: triggering hardware and software error handling paths to verify their integrity/existence • stress testing: push HW/SW to their limits • scrubbing: periodic restoration of potentially “decaying” hardware or software state • self-scrubbing data structures (like MVS) • ECC scrubbing for disks and memory

  25. Advantages of SON: 1 v. 2 Networks Physical Repair/Maintenance Die size vs. Clock rate, Complexity Silicon Die Cost ~ Area4 Cooling ~ (Watts/chip)N Size, Power Cost of System v. Cost of Disks Cluster advantages: dependability, scalability Advantages of CPU: Apps don’t parallelize, so 1 very fast CPU much better in practice than N fast CPUs Leverage Desktop MPU investment Software Maintenance: 1 Large system with several CPUs easier to install SW than several small computers CPU A Case for Storage Oriented Nodes

  26. CPU SON: 1 vs. 2 networks • Current computers all have LAN + Disk interconnect (SCSI, FCAL) • LAN is improving fastest, most investment, most features • SCSI, FCAL poor network features, improving slowly, relatively expensive for switches, bandwidth • Two sets of cables, wiring? • Why not single network based on best HW/SW technology?

  27. CPU SON: Physical Repair • Heterogeneous system with server components (CPU, backplane, memory cards, interface cards, power supplies, ...) and disk array components (disks, cables, controllers, array controllers, power supplies, ... ) • Keep all components available somewhere as FRUs • Homogeneous modules that is based on hot-pluggable interconnect (LAN) with Field Replacable Units: Node, Power Supplies, network cables • Replace node (disk, CPU, memory, NI) if any fail • Preventative maintenance via isolation, fault insertion

  28. CPU SON: Complexity v. Perf • Complexity increase: • HP PA-8500: issue 4 instructions per clock cycle, 56 instructions out-of-order execution, 4Kbit branch predictor, 9 stage pipeline, 512 KB I cache, 1024 KB D cache (> 80M transistors just in caches) • Intel SA-110: 16 KB I$, 16 KB D$, 1 instruction, in order execution, no branch prediction, 5 stage pipeline • Complexity costs in development time, development power, die size, cost • 440 MHz HP PA-8500 477 mm2, 0.25 micron/4M $330, > 40 Watts • 233 MHz Intel SA-110 50 mm2, 0.35 micron/3M $18, 0.4 Watts

  29. CPU Cost of System v. Disks • Examples show cost of way we build current systems (CPU, 2 networks, many disks/CPU …) Date Cost Disks Disks/CPU • NCR WorldMark: 10/97 $8.3M 1312 10.2 • Sun Enterprise 10k: 3/98 $5.2M 668 10.4 • Sun Enterprise 10k: 9/99 $6.2M 1732 27.0 • IBM Netinf. Cluster: 7/00 $7.8M 7040 55.0 • And these Data Base apps are CPU bound!!! • Also potential savings in space, power • ISTORE-1: with big switches, its 2-3 racks for 80 CPUs/disks (3/8 rack unit per CPU/disk themselves) • ISTORE-2: 4X density improvement?

  30. CPU SON: Cluster Advantages • Truly scalable architecture • Architecture that tolerates partial failure • Automatic hardware redundancy

  31. CPU SON: Cooling cost v. Peak Power • What is relationship? • Feet per second of air flow? • Packaging costs? • Fan failure?

  32. But: Assume Apps that parallelize: WWW services, Vision, Graphics Leverage investment in Embedded MPU, System on a Chip Improved maintenance is research target: e.g., many disks lower reliability, but RAID is better Advantages of CPU: Apps don’t parallelize, so N very fast CPU much better in practice than 2N fast CPUs Leverage Desktop MPU investment Software Installation: 1 Large system with several CPUs easier to keep SW up-to-date than several small computers CPU The Case for CPU

  33. Initial Applications • ISTORE is not one super-system that demonstrates all these techniques! • Initially provide middleware, library to support AME goals • Initial application targets • cluster web/email servers • self-scrubbing data structures, online self-testing • statistical identification of normal behavior • information retrieval for multimedia data • self-scrubbing data structures, structuring performance-robust distributed computation

  34. ISTORE Successor does Human Quality Vision? • Malik at UCB thinks vision research at critical juncture; have about right algorithms, awaiting faster computers to test them • 10,000 nodes with System-On-A-Chip + Microdrive + network • 1 to 10 GFLOPS/node => 10,000 to 100,000 GFLOPS • High Bandwidth Network • 1 to 10 GB of Disk Storage per Node => can replicate images per node • Need AME advances to keep 10,000 nodes useful

  35. Conclusions: ISTORE • Availability, Maintainability, and Evolutionary growth are key challenges for server systems • more important even than performance • ISTORE is investigating ways to bring AME to large-scale, storage-intensive servers • via clusters of network-attached, computationally-enhanced storage nodes running distributed code • via hardware and software introspection • we are currently performing application studies to investigate and compare techniques • Availability benchmarks a powerful tool? • revealed undocumented design decisions affecting SW RAID availability on Linux and Windows 2000 • Exciting applications for large systems that can be maintained

  36. Backup Slides

  37. TPC-D, TD V2, 10/97 32 nodes x 4 200 MHz CPUs, 1 GB DRAM, 41 disks (128 cpus, 32 GB, 1312 disks, 5.4 TB) CPUs, DRAM, encl., boards, power $5.3M Disks+cntlr $2.2M Disk shelves $0.7M Cables $0.1M HW total $8.3M Proc Proc Proc Proc Proc Proc Proc Proc Mem bridge bridge Mem Mem bus bridge bus bridge s c s i s c s i s c s i s c s i s c s i s c s i State of the art Cluster: NCR WorldMark BYNET switched network Bus Bus Mem pci pci … 32 1 … … … … … … … … … 64 source: www.tpc.org 1

  38. TPC-D,Oracle 8, 3/98 SMP 64 336 MHz CPUs, 64GB dram, 668 disks (5.5TB) Disks,shelf $2.1M Boards,encl. $1.2M CPUs $0.9M DRAM $0.8M Power $0.1M Cables,I/O $0.1M HW total $5.2M Proc Proc Proc Proc Proc Proc Proc Proc s s bridge bridge bus bridge bus bridge s c s i s c s i s c s i s c s i s c s i s c s i State of the Art SMP: Sun E10000 4 address buses data crossbar switch Xbar Xbar Mem Mem … 16 1 s c s i s c s i s c s i s c s i … … … … … … … … … 23 source: www.tpc.org 1

  39. TPC-C,Oracle 8i, 9/99 SMP 64 400 MHz CPUs, 64GB dram, 1732 disks (15.5TB) Disks,shelf $3.6M Boards,encl. $0.9M CPUs $0.9M DRAM $0.6M Power $0.1M Cables,I/O $0.1M HW total $6.2M Proc Proc Proc Proc Proc Proc Proc Proc s s bridge bridge bus bridge bus bridge fc a l fc a l fc a l fc a l fc a l fc a l State of the Art SMP: Sun E10000 4 address buses data crossbar switch Xbar Xbar Mem Mem … 16 1 fc a l fc a l fc a l fc a l … … … … … … … … source: www.tpc.org … 27 1

  40. TPC-C, DB2, 7/00 32 nodes x 4 700 MHz CPUs, 0.5 GB DRAM, 220 disks (128 cpus, 16 GB, 7040 disks, 116 TB) CPUs $0.6M Caches $0.5M DRAM $0.6M Disks $3.8M Disk shelves $1.6M Disk cntrl. $0.4M Racks $0.1M Cables $0.1M Switches $0.1M HW total $7.8M Proc Proc Proc Proc Proc Proc Proc Proc Mem bridge bridge Mem Mem bus bridge bus bridge s c s i s c s i s c s i s c s i s c s i s c s i State of the art Cluster: IBM Netinfinity Giganet 1Gbit switched Ethernet Bus Bus Mem pci pci … 32 1 … … … … … … … … … source: www.tpc.org 1 704

  41. Attacking Computer Vision • Analogy: Computer Vision Recognition in 2000 like Computer Speech Recognition in 1985 • Pre 1985 community searching for good algorithms: classic AI vs. statistics? • By 1985 reached consensus on statistics • Field focuses and makes progress, uses special hardware • Systems become fast enough that can train systems rather than preload information, which accelerates progress • By 1995 speech regonition systems starting to deploy • By 2000 widely used, available on PCs

  42. Computer Vision at Berkeley • Jitendra Malik believes has an approach that is very promising • 2 step process: 1) Segmentation: Divide image into regions of coherent color, texture and motion 2) Recognition: combine regions and search image database to find a match • Algorithms for 1) work well, just slowly (300 seconds per image using PC) • Algorithms for 2) being tested this summer using hundreds of PCs; will determine accuracy

  43. Human Quality Computer Vision • Suppose Algorithms Work: What would it take to match Human Vision? • At 30 images per second: segmentation • Convolution and Vector-Matrix Multiply of Sparse Matrices (10,000 x 10,000, 10% nonzero/row) • 32-bit Floating Point • 300 seconds on PC (assuming 333 MFLOPS) => 100G FL Ops/image • 30 Hz => 3000 GFLOPs machine to do segmentation

  44. Human Quality Computer Vision • At 1 / second: object recognition • Human can remember 10,000 to 100,000 objects per category (e.g., 10k faces, 10k Chinese characters, high school vocabulary of 50k words, ..) • To recognize a 3D object, need ~10 2D views • 100 x 100 x 8 bit (or fewer bits) per view=> 10,000 x 10 x 100 x 100 bytes or 109 bytes • Pruning using color and texture and by organizing shapes into an index reduce shape matches to 1000 • Compare 1000 candidate merged regions with 1000 candidate object images • If 10 hours on PC (333 MFLOPS) => 12000 GFLOPS • Use storage to reduce computation?

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