1. Seamless, Scalable Computing from Desktops to Global Computational Power Grids: Hype or Reality ?
Rajkumar Buyya
School of Computer Science and Software Engineering
Monash University, Melbourne, Australia |
Internet Home: |
Thanks to:
David Abramson
Jonathan Giddy
Jack Dongarra
Wolfgang Gentzsch

2. Agenda Computing Platforms and How Grid is Different ?
Cluster Computing Technologies and new challenges ?
Clusters of Clusters (Federated/Hyper Clusters)
Towards Global (Grid) Computing
Grid Resource Management and Scheduling Challenges
Grid Technologies (in development)
Grid Substrates and Interoperability
Grid Middleware
Resource Brokers and High Level Tools
Applications and Portals
Conclusion

3. Computing Power (HPC) Drivers
Solving grand challenge applications using computer modeling, simulation and analysis
E-commerce/anything
Life Sciences
Aerospace
Digital Biology
CAD/CAM
Military Applications
Military Applications
Military Applications

4. How to Run App. Faster ?
There are 3 ways to improve performance:
1. Work Harder
2. Work Smarter
3. Get Help
Computer Analogy
1. Use faster hardware: e.g. reduce the time per instruction (clock cycle).
2. Optimized algorithms and techniques
3. Multiple computers to solve problem: That is, increase no. of instructions executed per clock cycle.

5. Computing Platforms Evolution Breaking Administrative Barriers
2100 ?
PERFORMANCE
2100
Administrative Barriers
Individual
Group
Department
Campus
State
National
Globe
Inter Planet
Universe
Desktop
(Single Processor?)
SMPs or SuperComputers
Local
Cluster
Enterprise
Cluster/Grid
Global
Cluster/Grid
Inter Planet
Cluster/Grid ??

6. Towards Inexpensive Supercomputing
It is:
Cluster Computing..
The Commodity Supercomputing!

7. Clustering of Computers for Collective Computing: Trends
1990
1995+
1960

8. Clustering Today
Clustering is gained momentum when 4 technologies converged:
1. Killer Micros/CPUs: Very HP Microprocessors
workstation performance = yesterday supercomputers.
2. Killer Networks: High speed communication
Comm. between cluster nodes >= between processors in an SMP.
3. Killer Tools: Standard tools for parallel/ distributed computing & their growing popularity.
4. Killer Applications: scientific, engineering, Database, and Internet/Web serving.

9. Cluster Computer Architecture

10. Major issues in cluster design
Scalability (physical & application)
Performance
High Availability (fault-tolerance and failure management)
Single System Image (look-and-feel of one system)
Fast Communication (networks & protocols)
Load Balancing (CPU, Memory, Disk, Net)
Security and Encryption (clusters of clusters)
Distributed Environment (Social issues)
Manageability (administration and control)
Programmability (simple API if required)
Applicability (cluster-aware and non-aware apps.)

11. Killer Micro: Technology Trend (lead to the death of traditional supercomputers)

12. Killer Networks and Protocols
Ethernet (10Mbps),
Fast Ethernet (100Mbps),
Gigabit Ethernet (1Gbps)
SCI (Dolphin - MPI- 12micro-sec latency)
ATM
Myrinet (1.2Gbps)
Digital Memory Channel
Internet!!!
Protocols (Light weight protocols (User Level)
TCP/IP?
Active Messages (Berkeley)
Basic Interface for Parallelism (Lyon, France)
Fast Messages (Illinois)
U-net (Cornell)
VIA (Industry standard)…...

13. Killer Tools/Middleware (e.g., HKU JESSICA)

14. Killer Applications Numerous Scientific & Engineering Apps.
Parametric Simulations
Business Applications
E-commerce Applications (Amazon.com, eBay.com ….)
Database Applications (Oracle on cluster)
Decision Support Systems
Internet Applications
Web serving
Infowares (yahoo.com, AOL.com)
ASPs (application service providers)
eChat, ePhone, eBook, eCommerce, eBank, eSociety, eAnything!
Computing Portals
Mission Critical Applications
command control systems, banks, nuclear reactor control, star-war, and handling life threatening situations.

15. Science Portals - e.g., PAPIA system
Pentiums
Myrinet
NetBSD/Linuux PM
Score-D
MPC++
RWCP Japan:
PAPIA PC Cluster

16. Cluster Computing Forum
IEEE Task Force on Cluster Computing
(TFCC)

18. Adoption of the Approach

19. Clusters of Clusters (HyperClusters)
Scheduler
Master
Daemon
Execution
Submit
Graphical
Control
Clients
Cluster 2
Cluster 3
Cluster 1
LAN/WAN

22. Towards Grid Computing….
For illustration, placed resources arbitrarily on the GUSTO test-bed!!

23. What is Grid ? An infrastructure that couples
Computers (PCs, workstations, clusters, traditional supercomputers, and even laptops, notebooks, mobile computers, PDA, and so on) …
Software ? (e.g., ASPs renting expensive special purpose applications on demand)
Databases (e.g., transparent access to human genome database)
Special Instruments (e.g., radio Searching for Life in galaxy, for pulsars)
People (may be even animals who knows ?)
across the local/wide-area networks (enterprise, organisations, or Internet) and presents them as an unified integrated (single) resource.

24. Conceptual view of the Grid
Leading to Portal (Super)Computing

25. Grid Application-Drivers
Old and New applications getting enabled due to coupling of computers, databases, instruments, people, etc:
(distributed) Supercomputing
Collaborative engineering
high-throughput computing
large scale simulation & parameter studies
Remote software access / Renting Software
Data-intensive computing
On-demand computing

26. The Grid Vision: To offer
“Dependable, consistent, pervasive access to
[high-end] resources”
Dependable: Can provide performance and functionality guarantees
Consistent: Uniform interfaces to a wide variety of resources
Pervasive: Ability to “plug in” from anywhere
Source:

27. The Grid Impact!
“The global computational grid is expected to drive the economy of the 21st century similar to the electric power grid that drove the economy of the 20th century”

28. Sources of Complexity in Grid Resource Management
No single adminstrative control
No single policy
each resource owners have their own policies or scheduling mechanisms.
Users must honor them (particularly external users of the grid).
Heterogenity of resources (static and dynamic)
Unreliable - resource may come or disappear (die)
No uniform cost model (it cannot be)
varies from one user to another and time to time.
No Single access mechanism

29. Resource Management Challenges introduced by the GRID
Site Autonomy
Heteregenous Resources and Substrate
Each site has systems with different architectures (SMPs, Clusters.Linux/NT/Unix./Intel)
each resource owners have their own policies or scheduling mechanisms (Codine/Condor).
Policity Extensibility (through resource brokers)
Resource Allocation/Coallocation
Online Control (can Apps (Graphics) tolerate nonavailablity of required resource and adapt itself)
Computational Economy

30. Grid Resource Management: Challenging Issues
Authentication (once)
Specify simulation (code, resources, etc.)
Discover resources
Negotiate authorization, acceptable use, Cost, etc.
Acquire resources
Schedule Jobs
Initiate computation
Steer computation
Access remote data-sets
Collaborate on results
Account for usage
Domain 1
Domain 2
Ack.: globus..

35. Grid Components … … … … Grid Apps. Scientific Engineering
Applications and Portals
Grid
Apps. …
Scientific
Engineering
Collaboration
Prob. Solving Env.
Web enabled Apps
Development Environments and Tools
Grid
Tools …
Languages
Libraries
Debuggers
Monitoring
Resource Brokers
Web tools
Distributed Resources Coupling Services
Grid
Middleware …
Comm.
Sign on & Security
Information
Process
Data Access
QoS
Local Resource Managers
Operating Systems
Queuing Systems
Libraries & App Kernels …
TCP/IP & UDP
Grid
Fabric
Networked Resources across Organisations …
Computers
Clusters
Storage Systems
Data Sources
Scientific Instruments

36. Many GRID Projects and Initiatives
PUBLIC FORUMS
Computing Portals
Grid Forum
European Grid Forum
IEEE TFCC!
GRID’2000 and more.
Australia
Nimrod/G
EcoGrid and GRACE
DISCWorld
Europe
UNICORE
MOL
METODIS
Globe
Poznan Metacomputing
CERN Data Grid
MetaMPI
DAS
JaWS
and many more...
Public Grid Initiatives
Distributed.net
Compute Power Grid
USA
Globus
Legion
JAVELIN
AppLes
NASA IPG
Condor
Harness
NetSolve
NCSA Workbench
WebFlow
EveryWhere
and many more...
Japan
Ninf
Bricks

37. Distributed ASCI Supercomputer
Many GRID Testbeds...
GUSTO
Distributed ASCI Supercomputer
NASA IPG

38. Scheduler Architecture Alternatives
Scheduler Model
Scheduler Architecture
Example System
Centralized
(Single Resource) R
Unix, Linux,
Windows OS R1 R2 Rn .
Cluster systems
such as LSF,
Codine, Nimrod,
Condor
Centralized
(Multiple Resources)
(Single/Multiple Domains)
(Jobs)
(Queue) R
Multi-clusters or
Grid Systems
such as AppLes,
Nimrod/G, HTB,
Legion, Condor
Hierarchical
(Multiple Domains) R1
(Local Scheduler) .
(Resource Broker or
Super Scheduler) .
... R Rn R
Cluster systems like MOSIX follows simple model. Grid systems can follow, but it is complex to realize.
…….
Decentralized
(Self coordinated or Job Pool)
(Single/Multiple Domains) R1
(Job Pool) . . Rn

42. Grid Components and our Focus: Resource Brokers and Computational Economy
Apps.
Applications
High-level Services and Tools
GlobusView
Testbed Status
Grid
Tools
DUROC
MPI
MPI-IO
CC++
Nimrod/G
globusrun
Core Services
Grid
Middleware
Heartbeat Monitor
Nexus
GRAM
GRACE
Globus Security Interface
Gloperf
MDS
GASS
GARA
Grid
Fabric
Local Services
Condor
MPI
TCP
UDP
LSF
Easy
NQE
AIX
Irix
Solaris
Source: Globus

51. Nimrod/G Resource Broker
Nimrod/G Approach to Resource Management and Scheduling

52. What is Nimrod/G ?
A global scheduler for managing and steering task farming (parametric simulation) applications on computational grid based on deadline and computational economy.
Key Features
A single window to manage and control whole experiment
Resource Discovery
Trade for Resources
Scheduling
Steering & data management
Leverages Globus Services
Other parallel application models can be supported easily (MPI & DUROC co-allocation)

53. A Nimrod/G User Console
Deadline
Cost
Available
Machines

54. Nimrod/G Architecture
Nimrod/G Client
Nimrod/G Client
Nimrod/G Client
Nimrod Engine
Schedule Advisor
Persistent Store
Trading Manager
Dispatcher
Grid Explorer
TM TS
Middleware Services
GE GIS
Grid Information Services
RM & TS
RM & TS
RM & TS
GUSTO Test Bed
RM: Local Resource Manager, TS: Trade Server

55. Nimrod/G Interactions
Resource location
MDS
server
Additional services used implicitly:
GSI (authentication & authorization)
Nexus (communication)
Scheduler
Trade
Server
Resource allocation
(local)
Prmtc..
Engine
Dispatcher
GRAM
server
Queuing
System
Job
Wrapper
User
process
GASS
server
File access
Root node
Gatekeeper node
Computational node

56. Resource Location/Discovery
Need to locate suitable machines for an experiment
Speed
Number of processors
Cost
Availability
User account
Available resources will vary across experiment
Supported through Directory Server (Globus MDS)

57. Computational Economy
Resource selection on based real money and market based
A large number of sellers and buyers (resources may be dedicated/shared)
Negotiation: tenders/bids and select those offers meet the requirement
Trading and Advance Resource Reservation
Schedule computations on those resources that meet all requirements

58. Cost Model 1 3 2 User 5 Machine 1 User 1 Machine 5 non-uniform costing
time to time
one user to another
usage duration
encourages use of local resources first
user can access remote resources, but pays a penalty in higher cost.

59. Nimrod/G Scheduling Algorithm
Find a set of machines (MDS search)
Distribute jobs from root to machines
Establish job consumption rate for each machine
For each machine
Can we meet deadline?
If not, then return some jobs to root
If yes, distribute more jobs to resource
If cannot meet deadline with current resource
Find additional resources

60. Nimrod/G Scheduling algorithm ...
Locate
more
Machines
Locate
Machines
Establish
Rates
Re-distribute
Jobs
Distribute
Jobs
Meet
Deadlines?

61. Resource Usage (for various deadlines)

65. Scheduling Methods for Global Resource Allocation
1. Equal Assignment, but no Load Balancing
2. Equal Assignment and Load Balanced
3. (2) + deadline (no worry about cost)
4. (2) + deadline (minimize the cost of computation)
5. (4) + budget --> deadline + cost (up front agreement)
I am willing to pay $$$, can you complete by deadline.
6. (5) + Advance Resource Reservation
6. (6) + Grid Economy - dynamic pricing - use Trading/Tender/Bid process
7. (6) + Grid Economy - dynamic pricing - use auction technique
8. Genetic/Fuzzy Logic, etc algorithms

66. Grid Architecture for Computational Economy
GRACE
Grid Architecture for Computational Economy
GRACE aims help Nimrod/G overcome the current limitations.
GRACE middleware offer generic interfaces (APIs) that other developers of grid tools can use along with Globus services.

67. Grid Resource Management Architecture (Economic/Market-based)
Grid Information Server
Grid Explorer
Application
Job Control
Agent
Trading
Schedule Advisor
Trade Server
Other components of local resource manager
Resource
Reservation
Trade Manager
Deployment Agent
Resource Allocation R1 R2 … Rn
User
Resource Broker
A Resource Domain

68. Why Computational Economy in Resource Management ?
“Observe Grid characteristics and current resource management policies”
Grid resources are not owned by user or single organisation.
They have their own administrative policy
Mismatch in resource demand and supply
overall resource demand may exceed supply.
Traditional System-centric (performance matrix approaches does not suit in grid environment.
System-Centric --> User Centric
Like in real life, economic-based approach is one of the best ways to regulate selection and scheduling on the grid as it captures user-intent.
Markets are an effective institution in coordinating the activities of several entities.

69. Advantages of Economic-based RM
System Centric --> User Centric Policy in RM
Helps in regulating demand and supply
resource access cost can fluctuate (based on demand and supply and system can adapt)
Scalable Solution
No need of central coordinator (during negotiation)
Resources(sellers) and also Users(buyers) can make their own decisions and try to maximize utility and profit.
Uniform Treatment of all Resources
Everything can can be traded including CPU, Mem, Net, Storage/Disk, other devices/instruments
Efficient allocation of resources

70. Grid Open Trading Protocols
Trade Manager
Trade Server
Get Connected
Pricing Rules
Call for Bid(DT)
Reply to Bid (DT)
Negotiate Deal(DT) ….
API
Confirm Deal(DT, Y/N)
DT - Deal Template
- resource requirements (BM)
- resource profile (BS)
- price (any one can set)
- status
- change the above values
- negotiation can continue
- accept/decline
- validity period
Cancel Deal(DT)
Change Deal(DT)
Get Disconnected

71. Open Trading Finite State MachineDT
< TM, Request for Resource >
< TM, Ask Price >
<< TS, Update >>
<< TM, Update >> DT
<TS, Bid >
< TS, Final Offer >
< TM, Final Offer >
Offer TS
Offer TM
<TM, Rej.>
< TS, Reject >
< TM, Accept >
DT - Deal Template
TM - Trade Manager
TM - Trade Server
DA - Deal Accepted
DN - Deal Not accepted DA DN

73. Conclusions
The Emergence of Internet as a Powerful connectivity media is bridging the gap between a number of technologies leading to what is known as “Everything on IP”.
Cluster-based systems have become a platform of choice for mainstream computing.
Economic based approach to resource management is the way to go in the grid environment.
The user can say “I am willing to pay $…, can you complete my job by this time…”
Both sequential and parallel applications run seamless on desktops, SMPs, Clusters, and the Grid without any change.
Grid: A Next Generation Internet ?

74. Further Information Cluster Computing Infoware:
Grid Computing Infoware:
Millennium Compute Power Grid/Market Project
You are invited to join CPG project!
Books:
High Performance Cluster Computing, V1, V2, R.Buyya (Ed), Prentice Hall, 1999.
The GRID, I. Foster and C. Kesselman (Eds), Morgan-Kaufmann, 1999.
IEEE Task Force on Cluster Computing
GRID Forums |
GRID’2000 Meeting

75. Thank You… Any ??

76. Back up Slides Thanks to Jack Dongarra; He allowed me to
use his survey slides particularly those slides
which are cut and paste included.