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Service-Oriented Science:
Scaling eScience Impact
Ian Foster
Computation Institute
Argonne National Lab & University of Chicago
Acknowledgements
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Carl Kesselman, with whom I developed
many ideas (& slides)
Bill Allcock, Charlie Catlett, Kate Keahey,
Jennifer Schopf, Frank Siebenlist, Mike
Wilde @ ANL/UC
Ann Chervenak, Ewa Deelman, Laura
Pearlman @ USC/ISI
Karl Czajkowski, Steve Tuecke @ Univa
Numerous other fine colleagues in NESC,
EGEE, OSG, TeraGrid, etc.
NSF & DOE for research support
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Context:
System-Level Science
Problems too large &/or complex to tackle alone …
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Two Perspectives
on System-Level Science
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System-level problems require integration
‹
Of expertise
‹
Of data sources (“data deluge”)
‹
Of component models
‹
Of experimental modalities
‹
Of computing systems
Internet enables decomposition
‹
“When the network is as fast as the
computer's internal links, the machine
disintegrates across the net into a set of
special purpose appliances” (George Gilder)
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Integration & Decomposition:
A Two-Dimensional Problem
Function
Resource
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Decompose across network
Users
Discovery tools
Clients integrate dynamically
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‹
Select & compose services
‹
Select “best of breed” providers
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Publish result as new services
Analysis tools
Data Archives
Decouple resource & service providers Fig: S. G. Djorgovski
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A Unifying Concept:
The Grid
“Resource sharing & coordinated
problem solving in dynamic, multiinstitutional virtual organizations”
1.
Enable integration of distributed resources
2.
Using general-purpose protocols & infrastructure
3.
To deliver required quality of service
“The Anatomy of the Grid”, Foster, Kesselman, Tuecke, 2001
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System-Level
Problem
Decomposition
Implementation
Facilities
Computers
Storage
Networks
Services
Software
People
U. Colorado
UIUC
Experimental
Model
Experimental
Model
Grid technology
COORD.
NCSA
Computational Model
Service-Oriented Systems:
Applications vs. Infrastructure
Users
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‹
‹
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Composition
Service-oriented applications
Wrap applications as
services
Compose applications
into workflows
Service-oriented Grid
infrastructure
‹
Workflows
Invocation
Appln
Service
Appln
Service
Provisioning
Provision physical
resources to support
application workloads
“The Many Faces of IT as Service”, ACM Queue, Foster, Tuecke, 2005 8
Scaling eScience:
Forming & Operating Communities
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Define membership & roles; enforce laws &
community standards
‹
I.e., policy for service-oriented architecture
‹
Addressing dynamic membership & policy
Build, buy, operate, & share infrastructure
‹
‹
‹
Decouple consumer & provider
For data, programs, services, computing,
storage, instruments
Address dynamics of community demand
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Defining Community:
Membership and Laws
Identify VO participants and roles
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‹
For people and services
Specify and control actions of members
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‹
Empower members Æ delegation
‹
Enforce restrictions Æ federate
B
A
1
1
10
A
1
Effective
policy Access
Policy of site
to
community
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1
B
2
1
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2
Access
granted by
community
to user
Site
admissioncontrol
policies
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Evolution of Grid Security & Policy
1) Grid security infrastructure
‹
Public key authentication & delegation
‹
Access control lists (“gridmap” files)
Æ Limited set of policies can be expressed
2) Utilities to simplify operational use, e.g.
‹
MyProxy: online credential repository
‹
VOMS, ACL/gridmap management
Æ Broader set of policies, but still ad-hoc
3) General, standards-based framework for
authorization & attribute management
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Core Security Mechanisms
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Attribute Assertions
‹
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Authentication and digital signature
‹
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C asserts that S can perform O on behalf of C
Attribute mapping
‹
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Allows signer to assert attributes
Delegation
‹
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C asserts that S has attribute A with value V
{A1, A2… An}vo1 Ö {A’1, A’2… A’m}vo2
Policy
‹
Entity with attributes A asserted by C may
perform operation O on resource R
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Security Services for VO Policy
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Attribute Authority (ATA)
‹
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Issue signed attribute assertions
(incl. identity, delegation & mapping)
Authorization Authority (AZA)
‹
Decisions based on assertions & policy
VO
User A
VO
AZA
VO
ATA
VO
User B
VO A
Service
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Security Services for VO Policy
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Attribute Authority (ATA)
‹
z
Issue signed attribute assertions
(incl. identity, delegation & mapping)
Authorization Authority (AZA)
‹
Decisions based on assertions & policy
Delegation Assertion
VO
Resource Admin
User A User B can use Service A
Attribute
VO
AZA
VO
ATA
VO
User B
VO A
Service
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Security Services for VO Policy
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Attribute Authority (ATA)
‹
z
Issue signed attribute assertions
(incl. identity, delegation & mapping)
Authorization Authority (AZA)
‹
Decisions based on assertions & policy
Delegation Assertion
VO
Resource Admin
User A User B can use Service A
Attribute
VO
ATA
VO
AZA
VO M
embe
r Attr
ibute
VO Member
Attribute
VO
User B
VO A
Service
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Security Services for VO Policy
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Attribute Authority (ATA)
‹
z
Issue signed attribute assertions
(incl. identity, delegation & mapping)
Authorization Authority (AZA)
‹
Decisions based on assertions & policy
Delegation Assertion
VO
Resource Admin
User A User B can use Service A
Attribute
VO
ATA
Mapping
ATA
VO M
embe
r Attr
ibute
VO Member
Attribute
VO
User B
VO
AZA
VO A
Service
VO-A Attr Æ
VO-B Attr
VO B
Service
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Closing the Loop:
GT4 Security Toolkit
Authz Callout:
SAML, XACML
SSL/WS-Security
with Proxy
Services (running
Certificates
on user’s behalf)
Access
Compute
Center
Rights
CAS or VOMS
issuing SAML
or X.509 ACs
Users
Rights
Local policy
on VO identity
or attribute
authority
MyProxy
VO
Rights’
Shib KCA 17
Security Needn’t Be Hard:
Earth System Grid
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Purpose
‹
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Access to large data
Policies
‹
Per-collection control
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Different user classes
Implementation (GT)
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PURSE User Registration
Portal-based User
Registration Service
PKI, SAML assertions
Experience
‹
>2000 users
‹
>100 TB downloaded
Optional
review
See also:
GAMA (SDSC),
Dorian (OSU)
www.earthsystemgrid.org
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Scaling eScience:
Forming & Operating Communities
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Define membership & roles; enforce laws &
community standards
‹
I.e., policy for service-oriented architecture
‹
Addressing dynamics of membership & policy
Build, buy, operate, & share infrastructure
‹
‹
‹
Decouple consumer & provider
For data, programs, services, computing,
storage, instruments
Address dynamics of community demand
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Bootstrapping a VO
by Assembling Services
1) Integrate services from other sources
‹
Virtualize external services as VO services
Content
Services
Capacity
Community
Services
Provider
Capacity
Provider
2) Coordinate & compose
‹
Create new services from existing ones
“Service-Oriented Science”, Science, 2005
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Providing VO Services:
(1) Integration from Other Sources
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Negotiate service
level agreements
Delegate and deploy
capabilities/services
Community
A
…
Community
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Provision to deliver
defined capability
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Configure environment
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Host layered functions
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Virtualizing Existing Services
into a VO
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Establish service agreement with service
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E.g., WS-Agreement
Delegate use to VO user
User
A
VO User
User
B
VO Admin
Existing
Services
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Deploying New Services
Policy
Client
Allocate/provision
Configure
Initiate activity
Monitor activity
Control activity
Environment
Interface
Resource provider
Activity
WSRF (or WS-Transfer/WS-Man, etc.), Globus GRAM, Virtual Workspaces23
Available in High-Quality
Open Source Software …
Data
Replication
Globus Toolkit v4
www.globus.org
Credential
Mgmt
Replica
Location
Grid
Telecontrol
Protocol
Delegation
Data Access
& Integration
Community
Scheduling
Framework
WebMDS
Python
Runtime
Community
Authorization
Reliable
File
Transfer
Workspace
Management
Trigger
C
Runtime
Authentication
Authorization
GridFTP
Grid Resource
Allocation &
Management
Index
Java
Runtime
Security
Data
Mgmt
Execution
Mgmt
Info
Services
Common
Runtime
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Globus Toolkit Version 4: Software for Service-Oriented Systems, LNCS 3779, 2-13, 2005
http://dev.globus.org
Guidelines
(Apache)
Infrastructure
(CVS, email,
bugzilla, Wiki)
Projects
Include
…
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Virtual Workspaces
(Kate Keahey et al.)
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GT4 service for the creation, monitoring, &
management of virtual workspaces
High-level workspace description
Web Services interfaces for monitoring &
managing
Multiple implementations
‹
Dynamic accounts
‹
Xen virtual machines
‹
(VMware virtual machines)
Virtual clusters as a higher-level construct
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How do Grids and VMs Play Together?
request
VM EPR
VM Factory
create new VM image
Client
use existing VM image
inspect & manage
Create
VM image
VM Repository
deploy, suspend
VM Manager
Resource
VM
start program
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Virtual OSG Clusters
OSG
OSG cluster
Xen hypervisors
TeraGrid cluster
“Virtual Clusters for Grid Communities,”
Zhang et al., CCGrid 2006
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Dynamic Service Deployment
(Argonne + China Grid)
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Interface
‹
Upload-push
‹
Upload-pull
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Deploy
‹
Undeploy
‹
Reload
“HAND: Highly Available Dynamic
Deployment Infrastructure for GT4,”
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Li Qi et al., 2006
Providing VO Services:
(2) Coordination & Composition
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Take a set of provisioned services …
… & compose to synthesize new behaviors
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This is traditional service composition
‹
‹
But must also be concerned with emergent
behaviors, autonomous interactions
See the work of the agent & PlanetLab
communities
“Brain vs. Brawn: Why Grids and Agents Need Each Other," Foster,
Kesselman, Jennings, 2004.
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The Globus-Based
LIGO Data Grid
LIGO Gravitational Wave Observatory
Birmingham•
ƒCardiff
AEI/Golm
Replicating >1 Terabyte/day to 8 sites
>40 million replicas so far
MTBF = 1 month www.globus.org/solutions
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Data Replication Service
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Pull “missing” files to a storage system
Data Movement
Reliable
File
Transfer
Service
GridFTP
GridFTP
“Design and Implementation of a Data Replication Service Based on the Lightweight
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Data Replicator System,” Chervenak et al., 2005
Data Replication Service
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Pull “missing” files to a storage system
Data Location
Data Movement
Reliable
File
Transfer
Service
GridFTP
Local
Replica
Catalog
Replica
Location
Index
GridFTP
Local
Replica
Catalog
Replica
Location
Index
“Design and Implementation of a Data Replication Service Based on the Lightweight
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Data Replicator System,” Chervenak et al., 2005
Data Replication Service
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Pull “missing” files to a storage system
Data Location
Data Movement
Reliable
File
Transfer
Service
Data Replication
List of
required
Files
GridFTP
Local
Replica
Catalog
Replica
Location
Index
GridFTP
Local
Replica
Catalog
Replica
Location
Index
Data
Replication
Service
“Design and Implementation of a Data Replication Service Based on the Lightweight
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Data Replicator System,” Chervenak et al., 2005
Composing Resources …
Composing Services
Deploy service
Deploy container
Deploy virtual machine
Deploy hypervisor/OS
Procure hardware
GridFTP
JVM
VM
Hypervisor/OS
Physical machine
State exposed & access uniformly at all levels
Provisioning, management, and monitoring at all levels35
Composing Resources …
Composing Services
Deploy service
Deploy container
Deploy virtual machine
Deploy hypervisor/OS
Procure hardware
DRS
JVM
VM
GridFTP
LRC
GridFTP
VO Services
VM
Hypervisor/OS
Physical machine
State exposed & access uniformly at all levels
Provisioning, management, and monitoring at all levels36
Decomposition Enables
Separation of Concerns & Roles
S1
User
D
S3
“Provide access to
data D at S1, S2, S3
with performance P”
Service
Provider
“Provide storage
with performance P1,
network with P2, …”
Resource
Provider
S2
S1
D
S2
S3
Replica catalog,
User-level multicast, …
S1
D
S2
S3
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Another Example:
Astro Portal Stacking Service
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Purpose
‹
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On-demand “stacks”
of random locations
within ~10TB dataset
=
Challenge
‹
‹
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+
+
+
+
+
+
+
Rapid access to 1010K “random” files
Time-varying load
Solution
‹
Dynamic acquisition
of compute, storage
S4
Web page
or Web
Service
Sloan
Data
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Astro Portal
Stacking Performance (LAN GPFS)
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Summary
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Community based science will be the norm
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Many different types of communities
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Community infrastructure will increasingly
become the scientific observatory
Scaling requires a separation of concerns
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Differ in coupling, membership, lifetime, size
Must think beyond science stovepipes
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Requires collaborations across sciences—
including computer science
Providers of resources, services, content
Small set of fundamental mechanisms
required to build communities
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For More Information
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Globus Alliance
‹
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Dev.Globus
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www.opensciencegrid.org
TeraGrid
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dev.globus.org
Open Science Grid
‹
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www.globus.org
www.teragrid.org
Background
‹
2nd Edition
www.mkp.com/grid2
www.mcs.anl.gov/~foster
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