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
August 2000
Slide 1
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
Slide 2
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
“Computer Systems Research
Testing
-Past and Future”
Keynote address,
Performance
17th SOSP,
– Understanding when it doesn’t matter
Dec. 1999
Butler Lampson
Microsoft
Slide 3
Jim Gray: Trouble-Free Systems
•
•
Manager
“What Next?
– Sets goals
A dozen remaining IT problems”
– Sets policy
Turing Award Lecture,
– Sets budget
FCRC,
May 1999
– System does the rest.
Jim Gray
Everyone is a CIO
Microsoft
(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.
Slide 4
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:
»
»
1950’s
Today
(out less than 1 second per 100 years = 8 9’s of availability)
90% availability,
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.
Slide 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
“Back to the Future:
Time to Return to Longstanding
• Cost
Problems in Computer Systems?”
– per device and per service transaction Keynote address,
FCRC,
• Performance
May 1999
John Hennessy
– Remains important, but its not SPECint
Stanford
Slide 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 = 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
Slide 7
Is Maintenance the Key?
• Rule of Thumb: Maintenance 10X HW
– so over 5 year product life, ~ 95% of cost is maintenance
• VAX crashes ‘85, ‘93 [Murp95]; extrap. to ‘01
• Sys. Man.: N crashes/problem, SysAdmin action
– Actions: set params bad, bad config, bad app install
Slide 8
• HW/OS 70% in ‘85 to 28% in ‘93. In ‘01, 10%?
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
Slide 9
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
Slide 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
Slide 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
• 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
Slide 12
“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”
Slide 13
Example single-fault result
220
Solaris
215
210
1
205
Reconstruction
200
0
195
190
0
10
20
30
40
50
60
70
80
90
100
110
160
2
140
Reconstruction
120
#failures tolerated
Hits per second
Linux
2
1
Hits/sec
# failures tolerated
100
0
80
0
10
20
30
40
50
60
70
80
90
100
110
Time (minutes)
• 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
Slide 14
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, …)
Slide 15
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
Slide 16
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
Slide 17
ISTORE-1 hardware platform
• 80-node x86-based cluster, 1.4TB storage
– cluster nodes are plug-and-play, intelligent, networkattached 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
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...
Intelligent Disk “Brick”
Portable PC CPU: Pentium II/266 + DRAM
Redundant NICs (4 100 Mb/s links)
Diagnostic Processor
Disk
Half-height canister
Slide 18
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
Slide 19
A Case for
Intelligent Storage
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
Slide 20
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
Slide 21
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
Slide 22
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 $50k
Slide 23
Cost of Storage System v. Disks
• Hardware RAID box ~ 5X cost of disks
Slide 24
Disk Limit: Bus Hierarchy
CPU Memory
Server
bus
Memory
Internal
I/O bus
(PCI)
• Data rate vs. Disk rate
Storage Area
Network
(FC-AL)
RAID bus
Mem
External
– SCSI: Ultra3 (80 MHz),
Disk I/O
Wide (16 bit): 160 MByte/s
(SCSI)
– FC-AL: 1 Gbit/s = 125 MByte/sArray bus

Use only 50% of a bus
Command overhead (~ 20%)
 Queuing Theory (< 70%)

(15 disks/bus)
Slide 25
Physical Repair, Spare Parts
• ISTORE: Compatible modules based on hotpluggable interconnect (LAN) with few Field
Replacable Units (FRUs): Node, Power Supplies,
Switches, network cables
– Replace node (disk, CPU, memory, NI) if any fail
• Conventional: Heterogeneous system with many
server modules (CPU, backplane, memory cards,
…) and disk array modules (controllers, disks,
array controllers, power supplies, … )
– Store all components available somewhere as FRUs
– Sun Enterprise 10k has ~ 100 types of spare parts
– Sun 3500 Array has ~ 12 types of spare parts
Slide 26
ISTORE: Complexity v. Perf
• Complexity increase:
– HP PA-8500: issue 4 instructions per clock cycle, 56
instructions out-of-order execution, 4Kbit branch
predictor, 9 stage pipeline, 512 KB I cache, 1024 KB D
cache (> 80M transistors just in caches)
– Intel Xscale: 16 KB I$, 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/diskSlide 27
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, cluster
size 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
Slide 28
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
Slide 29
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
Slide 30
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
Slide 31
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
Slide 32
Questions?
Contact us if you’re interested:
email: patterson@cs.berkeley.edu
http://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
Slide 33
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)
Slide 34
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
Slide 35
10.
Enterprise 6500 Cluster Cluster 135
Cost of Storage System v. Disks
• Examples show cost of way we build current
systems (2 networks, many buses, CPU, …)
Date
Cost Main. Disks
/IObus
– NCR WM: 10/97 $8.3M
-- 1312
– Sun 10k:
3/98 $5.2M
-668
– Sun 10k:
9/99 $6.2M $2.1M 1732
– IBM Netinf: 7/00 $7.8M $1.8M 7040
=>Too complicated, too heterogenous
Disks Disks
/CPU
10.2
10.4
27.0
55.0
5.0
7.0
12.0
9.0
• And Data Bases are often CPU or bus bound!
– ISTORE disks per CPU:
– ISTORE disks per I/O bus:
1.0
1.0
Slide 36
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
Slide 37
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
Slide 38
available in redundant switches
SCSI v. IDE $/GB
• Prices from PC Magazine, 1995-2000
Slide 39
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
Slide 40
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
Slide 41
Benchmark Availability?
Methodology for reporting results
• Results are most accessible graphically
– plot change in QoS metrics over time
– compare to “normal” behavior?
Performance
» 99% confidence intervals calculated from no-fault runs
}
normal behavior
(99% conf)
injected
disk failure
0
reconstruction
Time
Slide 42