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ISTORE: A server for the PostPC Era

ISTORE: A server for the PostPC Era. Aaron Brown, Dave Martin, David Oppenheimer, Noah Trauhaft, Dave Patterson,Katherine Yelick University of California at Berkeley Patterson@cs.berkeley.edu UC Berkeley ISTORE Group istore-group@cs.berkeley.edu October 2000.

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ISTORE: A server for the PostPC Era

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  1. ISTORE: A server for the PostPC Era Aaron Brown, Dave Martin, David Oppenheimer, Noah Trauhaft, Dave Patterson,Katherine Yelick University of California at Berkeley Patterson@cs.berkeley.edu UC Berkeley ISTORE Group istore-group@cs.berkeley.edu October 2000

  2. ISTORE as Storage System of the Future • Availability, Maintainability, and Evolutionary growth key challenges for storage systems • Maintenance Cost ~ >10X Purchase Cost per year, • Even 2X purchase cost for 1/2 maintenance cost wins • AME improvement enables even larger systems • ISTORE also cost-performance advantages • Better space, power/cooling costs ($@colocation site) • More MIPS, cheaper MIPS, no bus bottlenecks • Compression reduces network $, encryption protects • Single interconnect, supports evolution of technology, single network technology to maintain/understand • Match to future software storage services • Future storage service software target clusters

  3. 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

  4. Jim Gray: Trouble-Free Systems “What Next? A dozen remaining IT problems” Turing Award Lecture, FCRC, May 1999 Jim Gray Microsoft • Manager • Sets goals • Sets policy • Sets budget • System does the rest. • Everyone is a CIO (Chief Information Officer) • Build a system • used by millions of people each day • Administered and managed by a ½ time person. • On hardware fault, order replacement part • On overload, order additional equipment • Upgrade hardware and software automatically.

  5. Jim Gray: Trustworthy Systems • Build a system used by millions of people that • Only services authorized users • Service cannot be denied (can’t destroy data or power). • Information cannot be stolen. • Is always available: (out less than 1 second per 100 years = 8 9’s of availability) • 1950’s 90% availability, Today 99% uptime for web sites, 99.99% for well managed sites (50 minutes/year)3 extra 9s in 45 years. • Goal: 5 more 9s: 1 second per century. • And prove it.

  6. 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

  7. 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 = 10-100 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

  8. VAX crashes ‘85, ‘93 [Murp95]; extrap. to ‘01 • Sys. Man.: N crashes/problem, SysAdminaction • Actions: set params bad, bad config, bad app install • HW/OS 70% in ‘85 to 28% in ‘93. In ‘01, 10%? Is Maintenance the Key? • Rule of Thumb: Maintenance 10X HW • so over 5 year product life, ~ 95% of cost is maintenance

  9. Principles for achieving AME • No single points of failure, lots of redundancy • Performance robustness is more important than peak performance • Performance can be sacrificed for improvements in AME • resources should be dedicated to AME • biological systems > 50% of resources on maintenance • can make up performance by scaling system • Introspection • reactive techniques to detect and adapt to failures, workload variations, and system evolution • proactive techniques to anticipate and avert problems before they happen

  10. CPU Hardware Techniques (1): SON • SON: Storage Oriented Nodes • 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

  11. 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

  12. 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 • 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

  13. “Hardware” culture (4) • 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”

  14. 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

  15. Deriving ISTORE • What is the interconnect? • FC-AL? (Interoperability? Cost of switches?) • Infiniband? (When? Cost of switches? Cost of NIC?) • Gbit Ehthernet? • Pick Gbit Ethernet as commodity switch, link • As main stream, fastest improving in cost performance • We assume Gbit Ethernet switches will get cheap over time (Network Processors, volume, …)

  16. Deriving ISTORE • Number of Disks / Gbit port? • Bandwidth of 2000 disk • Raw bit rate: 427 Mbit/sec. • Data transfer rate: 40.2 MByte/sec • Capacity: 73.4 GB • Disk trends • BW: 40%/year • Capacity, Areal density,$/MB: 100%/year • 2003 disks • ~ 500 GB capacity (<8X) • ~ 110 MB/sec or 0.9 Gbit/sec (2.75X) • Number of Disks / Gbit port = 1

  17. ISTORE-1 Brick • Webster’s Dictionary: “brick: a handy-sized unit of building or paving material typically being rectangular and about 2 1/4 x 3 3/4 x 8 inches” • ISTORE-1 Brick: 2 x 4 x 11 inches (1.3x) • Single physical form factor, fixed cooling required, compatible network interface to simplify physical maintenance, scaling over time • Contents should evolve over time: contains most cost effective MPU, DRAM, disk, compatible NI • If useful, could have special bricks (e.g., DRAM rich) • Suggests network that will last, evolve: Ethernet

  18. 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... 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

  19. Common Question: RAID? • Switched Network sufficient for all types of communication, including redundancy • Hierarchy of buses is generally not superior to switched network • Veritas, others offer software RAID 5 and software Mirroring (RAID 1) • Another use of processor per disk

  20. Advantages: Cost of Bandwidth Cost of Space Cost of Storage System v. Cost of Disks Physical Repair, Number of Spare Parts Cost of Processor Complexity Cluster advantages: dependability, scalability 1 v. 2 Networks A Case for Intelligent Storage

  21. Cost of Space, Power, Bandwidth • Co-location sites (e.g., Exodus) offer space, expandable bandwidth, stable power • Charge ~$1000/month per rack ( ~ 10 sq. ft.) • Includes 1 20-amp circuit/rack; charges ~$100/month per extra 20-amp circuit/rack • Bandwidth cost: ~$500 per Mbit/sec/Month

  22. Cost of Bandwidth, Safety • Network bandwidth cost is significant • 1000 Mbit/sec/month => $6,000,000/year • Security will increase in importance for storage service providers • XML => server format conversion for gadgets => Storage systems of future need greater computing ability • Compress to reduce cost of network bandwidth 3X; save $4M/year? • Encrypt to protect information in transit for B2B => Increasing processing/disk for future storage apps

  23. Cost of Space, Power • Sun Enterprise server/array (64CPUs/60disks) • 10K Server (64 CPUs): 70 x 50 x 39 in. • A3500 Array (60 disks): 74 x 24 x 36 in. • 2 Symmetra UPS (11KW): 2 * 52 x 24 x 27 in. • ISTORE-1: 2X savings in space • ISTORE-1: 1 rack (big) switches, 1 rack (old) UPSs, 1 rack for 80 CPUs/disks (3/8 VME rack unit/brick) • ISTORE-2: 8X-16X space? • Space, power cost/year for 1000 disks: Sun $924k, ISTORE-1 $484k, ISTORE2 $50k

  24. Cost of Storage System v. Disks • Hardware RAID box ~ 5X cost of disks

  25. Disk Limit: Bus Hierarchy Server Storage Area Network Memory bus CPU • Data rate vs. Disk rate • SCSI: Ultra3 (80 MHz), Wide (16 bit): 160 MByte/s • FC-AL: 1 Gbit/s = 125 MByte/s • Use only 50% of a bus • Command overhead (~ 20%) • Queuing Theory (< 70%) (FC-AL) Internal I/O bus Memory RAID bus (PCI) Mem External I/O bus Disk Array (SCSI) (15 disks/bus)

  26. Physical Repair, Spare Parts • ISTORE: Compatible modules based on hot-pluggable interconnect (LAN) with few Field Replacable Units (FRUs): Node, Power Supplies, Switches, network cables • Replace node (disk, CPU, memory, NI) if any fail • Conventional: Heterogeneous system with many server modules (CPU, backplane, memory cards, …) and disk array modules (controllers, disks, array controllers, power supplies, … ) • Store all components available somewhere as FRUs • Sun Enterprise 10k has ~ 100 types of spare parts • Sun 3500 Array has ~ 12 types of spare parts

  27. ISTORE: 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 Xscale: 16 KB I$, 16 KB D$, 1 instruction, in order execution, no branch prediction, 6 stage pipeline • Complexity costs in development time, development power, die size, cost • 550 MHz HP PA-8500 477 mm2, 0.25 micron/4M $330, 60 Watts • 1000 MHz Intel StrongARM2 (“Xscale”) @ 1.5 Watts, 800 MHz at 0.9 W, … 50 Mhz @ 0.01W, 0.18 micron (old chip 50 mm2, 0.35 micron, $18) • => Count $ for system, not processors/disk

  28. ISTORE: Cluster Advantages • Architecture that tolerates partial failure • Automatic hardware redundancy • Transparent to application programs • Truly scalable architecture • Given maintenance is 10X-100X capital costs, clustersize limits today are maintenance, floor space cost - generally NOT capital costs • As a result, it is THE target architecture for new software apps for Internet

  29. ISTORE: 1 vs. 2 networks • Current systems all have LAN + Disk interconnect (SCSI, FCAL) • LAN is improving fastest, most investment, most features • SCSI, FC-AL poor network features, improving slowly, relatively expensive for switches, bandwidth • FC-AL switches don’t interoperate • Two sets of cables, wiring? • SysAdmin trained in 2 networks, SW interface, …??? • Why not single network based on best HW/SW technology? • Note: there can be still 2 instances of the network (e.g. external, internal), but only one technology

  30. Initial Applications • ISTORE-1 is not one super-system that demonstrates all these techniques! • Initially provide middleware, library to support AME • Initial application targets • information retrieval for multimedia data (XML storage?) • self-scrubbing data structures, structuring performance-robust distributed computation • Example: home video server using XML interfaces • email service • self-scrubbing data structures, online self-testing • statistical identification of normal behavior

  31. 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: • 2006 brick: System On a Chip integrated with MicroDrive • 9GB disk, 50 MB/sec from disk • connected via crossbar switch • From brick to “domino” • If low power, 10,000 nodes fit into one rack! • O(10,000) scale is our ultimate design point

  32. Conclusion: ISTORE as Storage System of the Future • Availability, Maintainability, and Evolutionary growth key challenges for storage systems • Maintenance Cost ~ 10X Purchase Cost per year, so over 5 year product life, ~ 95% of cost of ownership • Even 2X purchase cost for 1/2 maintenance cost wins • AME improvement enables even larger systems • ISTORE has cost-performance advantages • Better space, power/cooling costs ($@colocation site) • More MIPS, cheaper MIPS, no bus bottlenecks • Compression reduces network $, encryption protects • Single interconnect, supports evolution of technology, single network technology to maintain/understand • Match to future software storage services • Future storage service software target clusters

  33. Questions? Contact us if you’re interested:email: patterson@cs.berkeley.eduhttp://iram.cs.berkeley.edu/ “If it’s important, how can you say if it’s impossible if you don’t try?” Jean Morreau, a founder of European Union

  34. Clusters and TPC Software 8/’00 • TPC-C: 6 of Top 10 performance are clusters, including all of Top 5; 4 SMPs • TPC-H: SMPs and NUMAs • 100 GB All SMPs (4-8 CPUs) • 300 GB All NUMAs (IBM/Compaq/HP 32-64 CPUs) • TPC-R: All are clusters • 1000 GB :NCR World Mark 5200 • TPC-W: All web servers are clusters (IBM)

  35. Clusters and TPC-C Benchmark Top 10 TPC-C Performance (Aug. 2000) Ktpm 1. Netfinity 8500R c/s Cluster 441 2. ProLiant X700-96P Cluster 262 3. ProLiant X550-96P Cluster 230 4. ProLiant X700-64P Cluster 180 5. ProLiant X550-64P Cluster 162 6. AS/400e 840-2420 SMP 152 7. Fujitsu GP7000F Model 2000 SMP 139 8. RISC S/6000 Ent. S80 SMP 139 9. Bull Escala EPC 2400 c/s SMP 136 10. Enterprise 6500 Cluster Cluster 135

  36. Cost of Storage System v. Disks • Examples show cost of way we build current systems (2 networks, many buses, CPU, …) Disks Disks Date Cost Main. Disks /CPU /IObus • NCR WM: 10/97 $8.3M -- 1312 10.2 5.0 • Sun 10k: 3/98 $5.2M -- 668 10.4 7.0 • Sun 10k: 9/99 $6.2M $2.1M 1732 27.0 12.0 • IBM Netinf: 7/00 $7.8M $1.8M 7040 55.0 9.0 =>Too complicated, too heterogenous • And Data Bases are often CPU or bus bound! • ISTORE disks per CPU: 1.0 • ISTORE disks per I/O bus: 1.0

  37. Common Question: Why Not Vary Number of Processors and Disks? • Argument: if can vary numbers of each to match application, more cost-effective solution? • Alternative Model 1: Dual Nodes + E-switches • P-node: Processor, Memory, 2 Ethernet NICs • D-node: Disk, 2 Ethernet NICs • Response • As D-nodes running network protocol, still need processor and memory, just smaller; how much save? • Saves processors/disks, costs more NICs/switches: N ISTORE nodes vs. N/2 P-nodes + N D-nodes • Isn't ISTORE-2 a good HW prototype for this model? Only run the communication protocol on N nodes, run the full app and OS on N/2

  38. Common Question: Why Not Vary Number of Processors and Disks? • Alternative Model 2: N Disks/node • Processor, Memory, N disks, 2 Ethernet NICs • Response • Potential I/O bus bottleneck as disk BW grows • 2.5" ATA drives are limited to 2/4 disks per ATA bus • How does a research project pick N? What’s natural? • Is there sufficient processing power and memory to run the AME monitoring and testing tasks as well as the application requirements? • Isn't ISTORE-2 a good HW prototype for this model? Software can act as simple disk interface over network and run a standard disk protocol, and then run that on N nodes per apps/OS node. Plenty of Network BW available in redundant switches

  39. SCSI v. IDE $/GB • Prices from PC Magazine, 1995-2000

  40. Grove’s Warning “...a strategic inflection point is a time in the life of a business when its fundamentals are about to change. ... Let's not mince words: A strategic inflection point can be deadly when unattended to. Companies that begin a decline as a result of its changes rarely recover their previous greatness.” Only the Paranoid Survive, Andrew S. Grove, 1996

  41. 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

  42. 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

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