1. Building PetaByte Servers
Jim Gray
Microsoft Research
Kilo 103
Mega 106
Giga 109
Tera today, we are here
Peta 1015
Exa 1018

2. Outline The challenge: Building GIANT data stores Conclusion 1
for example, the EOS/DIS 15 PB system
Conclusion 1
Think about MOX and SCANS
Conclusion 2:
Think about Clusters
SMP report
Cluster report

3. The Challenge -- EOS/DIS
Antarctica is melting -- 77% of fresh water liberated
sea level rises 70 meters
Chico & Memphis are beach-front property
New York, Washington, SF, LA, London, Paris
Let’s study it! Mission to Planet Earth
EOS: Earth Observing System (17B$ => 10B$)
50 instruments on 10 satellites
Landsat (added later)
EOS DIS: Data Information System:
3-5 MB/s raw, MB/s processed.
4 TB/day,
15 PB by year 2007

4. The Process Flow Data arrives and is pre-processed.
instrument data is calibrated, gridded averaged
Geophysical data is derived
Users ask for stored data OR to analyze and combine data.
Can make the pull-push split dynamically
Pull Processing
Push Processing
Other Data

5. Designing EOS/DIS
Expect that millions will use the system (online) Three user categories:
NASA funded by NASA to do science
Global Change 10 k - other dirt bags
Internet 20 m - everyone else
Grain speculators
Environmental Impact Reports
New applications => discovery & access must be automatic
Allow anyone to set up a peer- node (DAAC & SCF)
Design for Ad Hoc queries, Not Standard Data Products If push is 90%, then 10% of data is read (on average).
=> A failure: no one uses the data, in DSS, push is 1% or less.
=> computation demand is enormous (pull:push is 100: 1)

6. Obvious Points: EOS/DIS will be a cluster of SMPs
It needs 16 PB storage
= 1 M disks in current technology
= 500K tapes in current technology
It needs 100 TeraOps of processing
= 100K processors (current technology)
and ~ 100 Terabytes of DRAM
1997 requirements are 1000x smaller
smaller data rate
almost no re-processing work

7. The architecture 2+N data center design Scaleable OR-DBMS
Emphasize Pull vs Push processing
Storage hierarchy
Data Pump
Just in time acquisition

8. 2+N data center design duplex the archive (for fault tolerance)
let anyone build an extract (the +N)
Partition data by time and by space (store 2 or 4 ways).
Each partition is a free-standing OR-DBBMS (similar to Tandem, Teradata designs).
Clients and Partitions interact via standard protocols
OLE-DB, DCOM/CORBA, HTTP,…

9. Hardware Architecture
2 Huge Data Centers
Each has 50 to 1,000 nodes in a cluster
Each node has about 25…250 TB of storage
SMP Bips to 50 Bips K$
DRAM 50GB to 1 TB K$
100 disks TB to 230 TB 200K$
10 tape robots 25 TB to 250 TB 200K$
2 Interconnects 1GBps to 100 GBps 20K$
Node costs 500K$
Data Center costs 25M$ (capital cost)

10. Scaleable OR-DBMS
Adopt cluster approach (Tandem, Teradata, VMScluster, DB2/PE, Informix,....)
System must scale to many processors, disks, links
OR DBMS based on standard object model
CORBA or DCOM (not vendor specific)
Grow by adding components
System must be self-managing

11. Storage Hierarchy Cache hot 10% (1.5 PB) on disk.
Keep cold 90% on near-line tape.
Remember recent results on speculation
15 PB of Tape Robot
1 PB of Disk
10-TB RAM
500 nodes
10,000 drives
4x1,000 robots

12. Data Pump Some queries require reading ALL the data (for reprocessing)
Some queries require reading ALL the data (for reprocessing)
Each Data Center scans the data every 2 weeks.
Data rate 10 PB/day = 10 TB/node/day = 120 MB/s
Compute on demand small jobs
less than 1,000 tape mounts
less than 100 M disk accesses
less than 100 TeraOps.
(less than 30 minute response time)
For BIG JOBS scan entire 15PB database
Queries (and extracts) “snoop” this data pump.

13. Just-in-time acquisition 30%
Hardware prices decline 20%-40%/year
So buy at last moment
Buy best product that day: commodity
Depreciate over 3 years so that facility is fresh.
(after 3 years, cost is 23% of original). 60% decline peaks at 10M$
EOS DIS Disk Storage Size and Cost 1 10 2 3 4 5
assume 40% price decline/year
Data Need TB
Storage Cost M$
1994
1996
1998
2000
2002
2004
2006
2008

14. Problems HSM Design and Meta-data Ingest
Data discovery, search, and analysis
reorg-reprocess
disaster recovery
cost

15. Trends: New Applications
The Old World:
Millions of objects
100-byte objects
Trends: New Applications
The New World:
Billions of objects
Big objects (1MB)
Multimedia:
Text, voice, image, video, ...
The paperless office
Library of congress online (on your campus)
All information comes electronically
entertainment
publishing
business
Information Network,
Knowledge Navigator,
Information at Your Fingertips

16. What's a Terabyte Terror Byte !! .1% of a PetaByte!!!!!!!!!!!!!!!!!!
1,000,000,000 business letters
100,000,000 book pages
50,000,000 FAX images
10,000,000 TV pictures (mpeg)
4,000 LandSat images
Library of Congress (in ASCI) is 25 TB
1980: 200 M$ of disc ,000 discs
5 M$ of tape silo ,000 tapes
1994: M$ of magnetic disc discs
500 K$ of optical disc robot platters
50 K$ of tape silo tapes
Terror Byte !!
.1% of a PetaByte!!!!!!!!!!!!!!!!!!
150 miles of bookshelf
15 miles of bookshelf
7 miles of bookshelf
10 days of video

17. The Cost of Storage & Access
File Cabinet: cabinet (4 drawer) 250$ paper (24,000 sheets) 250$ space 10$/ft2) 180$ total $ ¢/sheet
Disk: disk (9 GB =) ,000$ ASCII: m pages ¢/sheet (100x cheaper)
Image: 200 k pages ¢/sheet (similar to paper)

18. Standard Storage Metrics
Capacity:
RAM: MB and $/MB: today at 100 MB & 10 $/MB
Disk: GB and $/GB: today at 10 GB and 200 $/GB
Tape: TB and $/TB: today at .1 TB and 100 k$/TB (nearline)
Access time (latency)
RAM: 100 ns
Disk: ms
Tape: second pick, 30 second position
Transfer rate
RAM: GB/s
Disk: MB/s Arrays can go to 1GB/s
Tape: MB/s not clear that striping works

19. New Storage Metrics: KOXs, MOXs, GOXs, SCANs?
KOX: How many kilobyte objects served per second
the file server, transaction processing metric
MOX: How many megabyte objects served per second
the Mosaic metric
GOX: How many gigabyte objects served per hour
the video & EOSDIS metric
SCANS: How many scans of all the data per day
the data mining and utility metric

20. Summary (of new ideas) Storage accesses are the bottleneck
Accesses are getting larger (MOX, GOX, SCANS)
Capacity and cost are improving
BUT
Latencies and bandwidth are not improving much SO
Use parallel access (disk and tape farms)

21. How To Get Lots of MOX, GOX, SCANS
parallelism: use many little devices in parallel
Beware of the media myth
Beware of the access time myth
At 10 MB/s: 1.2 days to scan
1,000 x parallel: 1.5 minute SCAN.
1 Terabyte
1 Terabyte
10 MB/s
Parallelism: divide a big problem into many smaller ones to be solved in parallel.

22. Meta-Message: Technology Ratios Are Important
If everything gets faster&cheaper at the same rate then nothing really changes.
Some things getting MUCH BETTER:
communication speed & cost 1,000x
processor speed & cost 100x
storage size & cost 100x
Some things staying about the same
speed of light (more or less constant)
people (10x worse)
storage speed (only 10x better)

23. Outline The challenge: Building GIANT data stores Conclusion 1
for example, the EOS/DIS 15 PB system
Conclusion 1
Think about MOX and SCANS
Conclusion 2:
Think about Clusters
SMP report
Cluster report

24. Scaleable Computers BOTH SMP and Cluster
Grow Up with SMP
4xP6 is now standard
Grow Out with Cluster
Cluster has inexpensive parts
SMP
Super Server
Departmental
Cluster
of PCs
Server
Personal
System

25. TPC-C Current Results
Best Performance is 30,390 $305/tpmC (Oracle/DEC)
Best Price/Perf. is 7,693 $43.5/tpmC (MS SQL/Dell)
Graphs show
UNIX high price
UNIX scaleup diseconomy

26. Compare SMP Performance

27. TPC C improved fast
40% hardware,
100% software,
100% PC Technology

28. Where the money goes

29. What does this mean? PC Technology is 3x cheaper than high-end SMPs
PC nodes performance are 1/2 of high-end SMPs
4xP6 vs 20xUltraSparc
Peak performance is a cluster
Tandem 100 node cluster
DEC Alpha 4x8 cluster
Commodity solutions WILL come to this market

30. Cluster: Shared What? Shared Memory Multiprocessor Shared Disk Cluster
Multiple processors, one memory
all devices are local
DEC, SG, Sun Sequent nodes
easy to program, not commodity
Shared Disk Cluster
an array of nodes
all shared common disks
VAXcluster + Oracle
Shared Nothing Cluster
each device local to a node
ownership may change
Tandem, SP2, Wolfpack

31. Clusters being built Teradata 1500 nodes +24 TB disk (50k$/slice)
Tandem,VMScluster 150 nodes (100k$/slice)
Intel, 9,000 55M$ ( 6k$/slice)
Teradata, Tandem, DEC moving to NT+low slice price
IBM: m$ (200k$/slice)
PC clusters (bare handed) at dozens of nodes web servers (msn, PointCast,…), DB servers
KEY TECHNOLOGY HERE IS THE APPS.
Apps distribute data
Apps distribute execution

32. Cluster Advantages Clients and Servers made from the same stuff.
Inexpensive: Built with commodity components
Fault tolerance:
Spare modules mask failures
Modular growth
grow by adding small modules
Parallel data search
use multiple processors and disks

33. Clusters are winning the high end
You saw that a 4x8 cluster has best TPC-C performance
This year, a 95xUltraSparc cluster won the MinuteSort Speed Trophy (see NOWsort at
Ordinal 16x on SGI Origin is close (but the loser!).

34. Clusters (Plumbing) Single system image Fault Tolerance
naming
protection/security
management/load balance
Fault Tolerance
Wolfpack Demo
Hot Pluggable hardware & Software

35. So, What’s New? When slices cost 50k$, you buy 10 or 20.
Manageability, programmability, usability become key issues (total cost of ownership).
PCs are MUCH easier to use and program
MPP
Vicious Cycle
No Customers!
New
MPP &
NewOS
App
Apps
CP/Commodity
Virtuous Cycle:
Standards allow progress
and investment protection
Standard
OS & Hardware
Customers

36. Windows NT Server Clustering High Availability On Standard Hardware
Standard API for clusters on many platforms
No special hardware required.
Resource Group is unit of failover
Typical resources:
shared disk, printer, ...
IP address, NetName
Service (Web,SQL, File, Print Mail,MTS
API to define
resource groups,
dependencies,
resources,
GUI administrative interface
A consortium of 60 HW & SW vendors (everybody who is anybody)
2-Node Cluster in beta test now.
Available 97H1
>2 node is next
SQL Server and Oracle Demo on it today
Key concepts
System: a node
Cluster: systems working together
Resource: hard/ soft-ware module
Resource dependency: resource needs another
Resource group: fails over as a unit
Dependencies: do not cross group boundaries
The Wolfpack program has three goals: (1) To be the most reliable way to run Windows NT Server, (2) to be the most cost-effective high-availability platform, and (3) to be the easiest platform for developing cluster-aware solutions. Let’s look at each of those three in more detail.
Wolfpack will be the most reliable way to run Windows NT Server. Out of the box, it will provide automatic recovery for file sharing, printer sharing, and Internet/Intranet services. It will be able to provide basic recovery services for virtually any existing server application without coding changes, and will feature an administrator’s console that makes it easy to take a server off-line for maintenance without disrupting your mission-critical business applications. The other server can deliver services while one is being changed.
Wolfpack will run on standard servers from many vendors. It can use many interconnects ranging from standard Ethernet to specialized high-speed ones like Tandem ServerNet. It works with a wide range of disk drives and controllers including standard SCSI drives. This broad hardware support means flexibility, choice, and competitive pricing. Wolfpack clustering technology allows all nodes in the cluster to do useful work -- there’s no wasted “standby” server sitting idle waiting for a failure as there is with server mirroring solutions. And, of course, because it’s Windows software, it will have a familiar and easy to use graphical interface for the administrator.
SQL Server will use Wolfpack’s Clustering API to provide high-availability via disk and IP address failover. SQL Server continues its close integration with NT and its unmatched ease-of-use. SQL Server 7.0 will provide a GUI configuration and management wizard to make it easy to configure high availability databases.

37. Wolfpack NT Clusters 1.0 B A Two node file and print failover Clients
Private
Private
Shared SCSI Disk Strings
Disks
Disks B A
etty
lice
Clients
GUI admin interface
Wolfpack NT Clusters 1.0 supports clusters containing two nodes, affectionately called Alice and Betty. Alice and Betty have some private devices and some shared SCSI strings. At any instant, each SCSI disk is “owned” by either Alice or Betty. The SCSI II commands are used to implement this ownership. Most modern SCSI controllers and disks correctly implement these commands. Microsoft is qualifying many controller and disk vendors for Wolfpack.
In configuring Wolfpack NT Clusters, the operator assigns shared devices to one or another failover Resource Groups. During normal operation one failover group is served by Alice and the other group is served by Betty. In case one node fails, the other node takes ownership of the shared devices in that resource group and starts serving them. When the failed node returns to service, it can resume ownership of the resource group.
Resources in the group can be disks, services, IP addresses, SQL databases, and other resources. The Wolfpack API allows any application to declare itself as a resource and participate in a Resource Group
The cluster administration interface provides a graphical way to define resource groups and resources. It also provides a way to monitor and control the resource groups.

38. What is Wolfpack? Cluster Service Resource Management Interface
Cluster Management Tools
Cluster Api DLL
RPC
Cluster Service
Global Update
Database
Manager
Manager
Node
Event Processor
Manager
Failover Mgr
Communication
App
Resource
Mgr
Manager
Resource
Other Nodes
DLL
Open
Online
IsAlive
LooksAlive
Offline
Close
Resource
Resource Monitors
Management
Interface
Physical
Logical
App
Non Aware
App
Resource
Resource
Resource
DLL
DLL
DLL
Cluster Aware
App

39. Where We Are Today Clusters moving fast Technology ahead of schedule
OLTP
Sort
WolfPack
Technology ahead of schedule
cpus, disks, tapes,wires,..
OR Databases are evolving
Parallel DBMSs are evolving
HSM still immature

40. Outline The challenge: Building GIANT data stores Conclusion 1
for example, the EOS/DIS 15 PB system
Conclusion 1
Think about MOX and SCANS
Conclusion 2:
Think about Clusters
SMP report
Cluster report

41. Building PetaByte Servers
Jim Gray
Microsoft Research
Kilo 103
Mega 106
Giga 109
Tera today, we are here
Peta 1015
Exa 1018