QoS-based Scheduling of e-Research Application Workflows on Global Grids
Dr. Rajkumar Buyya
Grid Computing and Distributed Systems (GRIDS) LaboratoryDept. of Computer Science and Software EngineeringThe University of Melbourne, Australiawww.gridbus.org
Gridbus Sponsors
2
GRIDS Lab @ Melbourne
Youngest and one of the rapidly growing research labs in our School/University:
Founded in 2002 Houses:
Research Fellows/PostDocs Research Programmers PhD candidates Honours/Masters students
Funding National and International organizations Australian Research Council & DEST Many industries (Sun, StorageTek, Microsoft,
IBM, Microsoft) University-wide collaboration:
Faculties of Science, Engineering, and Medicine
Many national and international collaborations.
Academics Industries
Software: Widely in academic and industrial users.
Publication: My research team produces over 20% of our
Dept’s research output.
EducationR & D
+ Community Services: e.g., IEEE TC for Scalable Computing
3
Agenda
Introduction Utility Networks and Grid Computing Application Drivers and Various Types of Grid Services
Global Grids and Challenges Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Market-based Management and Gridbus Software Stack
Grid Workflows and QoS Scheduling Architecture, Design and Implementation Performance Evaluation: Simulation based workflows
SLA-based Resource Allocation Utility based allocation, pricing, performance results
Summary and Conclusion
4
Power Grid Inspiration: Seamlessly delivering electricity as a utility to users
5
(5) Computing Grid: Delivering IT services as the 5th utility after water, gas, electricity, and
telephone
eScienceeBusiness
eGovernmenteHealth
MultilingualeEducation
…
6
Grid-like Vision
In 1969, Leonard Kleinrock, one of the chief scientists of the original ARPA project which seeded the Internet, wrote:
"As of now, computer networks are still in their infancy, but as they grow up and become sophisticated, we will probably see the spread of "computer utilities", which, like present electric and telephone utilities, will service individual homes and offices across the country“
Despite major advances in hardware and software systems over the past 35 years, we are yet to realize this vision. How far are we still from delivering computing as a utility?
7
Computing and Communication Technologies Evolution: 1960-2010!
* Sputnik
1960 1970 1975 1980 1985 1990 1995 2000
* ARPANET
* Email* Ethernet
* TCP/IP* IETF
* Internet Era * WWW Era
* Mosaic
* XML
* PC Clusters* Crays * MPPs
* Mainframes
* HTML
* W3C
* P2P
* Grids
* XEROX PARC wormCO
MP
UTIN
GC
om
mu
nic
ati
on
* Web Services
* Minicomputers
* PCs
* WS Clusters
* PDAs* Workstations
* HTC
2010
* e-Science
* Computing as Utility
* e-Business
* SocialNet
ControlCentralised Decentralised
8
What is Grid? (It means different things to different people)
IBM On Demand Computing
Microsoft .NET
Oracle 10g
Sun N1 – Sun Grid Engine
HP Adaptive Enterprise
Amazon Electric Cloud Services
United Devices and related companies: Harvesting Unused Desktop resources
9
What is Grid?[Buyya et. al.]
A type of parallel and distributed system that enables the sharing, exchange, selection, & aggregation of geographically distributed “autonomous” resources:
Computers – PCs, workstations, clusters, supercomputers, laptops, notebooks, mobile devices, PDA, etc;
Software – e.g., ASPs renting expensive special purpose applications on demand;
Catalogued data and databases – e.g. transparent access to human genome database;
Special devices/instruments – e.g., radio telescope – SETI@Home searching for life in galaxy.
People/collaborators.
depending on their availability, capability, cost, and user QoS requirements.
Widearea
10
How does Grids look like?A Bird Eye View of a Global Grid
Grid Resource Broker
Resource Broker
Application
Grid Information Service
Grid Resource Broker
databaseR2R3
RN
R1
R4
R5
R6
Grid Information Service
11
Classes of Grid Services / Types of Grids
Computational Services – CPU cycles Pooling computing power: SETI@Home, TeraGrid,
AusGrid, ChinaGrid, IndiaGrid, UK Grid,… Data Services
Collaborative data sharing generated by instruments, sensors, persons: LHC Grid, Napster
Application Services Access to remote software/libraries and license
management—NetSolve Interaction Services
eLearning, Virtual Tables, Group Communication (Access Grid), Gaming
Knowledge Services The way knowledge is acquired, processed and
managed—data mining. Utility Computing Services
Towards a market-based Grid computing: Leasing and delivering Grid services as ICT utilities.
Computational Grid
Data Grid
ASP Grid
Interaction Grid
Knowledge Grid
Utility Grid
infra
stru
ctu
re
Users
12
How Are Grids Used?
High-performance computing
Collaborative data-sharing
Collaborative design
Drug discovery
Financial modeling
Data center automation
High-energy physics
Life sciences
E-Business
E-ScienceNatural language processing & Data Mining
Utility computing
13
e-Science Environment: Supporting Collaborative Science
Distributed instruments
Distributed computation
Distributed data
Peers sharing ideas and collaborative
interpretation of data/results
2100 2100 2100 2100
2100 2100 2100 2100
Remote Visualization
Data & Compute Service
Cyberinfrastructure
E-Scientist
14
Agenda
Introduction Utility Networks and Grid Computing Application Drivers and Various Types of Grid Services
Global Grids and Challenges Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Market-based Management and Gridbus Software Stack
Grid Workflows and QoS Scheduling Architecture, Design and Implementation Performance Evaluation: Simulation based workflows
SLA-based Resource Allocation Utility based allocation, pricing, performance results
Summary and Conclusion
15
Grid Challenges
Security
Resource Allocation & Scheduling
Data locality
Network Management
System Management
Resource Discovery
Uniform Access
Computational Economy
Application Construction
16
Some Grid Initiatives Worldwide
Australia Nimrod-G Gridbus DISCWorld GrangeNet. APACGrid ARC eResearch
Brazil OurGrid, EasyGrid LNCC-Grid + many others
China ChinaGrid – Education CNGrid - application
Europe UK eScience EU Grids.. and many more...
India Garuda
Japan NAGERI
Korea...N*Grid
SingaporeNGP
USA Globus GridSec AccessGrid TeraGrid Cyberinfrasture and many more...
Industry Initiatives IBM On Demand Computing HP Adaptive Computing Sun N1 Microsoft - .NET Oracle 10g Infosys – Enterprise Grid Satyam – Business Grid StorageTek –Grid.. and many more
Public Forums Global Grid Forum Australian Grid Forum Conferences:
CCGrid Grid HPDC E-Science
http://www.gridcomputing.com
1.3 billion – 3 yrs
1 billion – 5 yrs
450million – 5 yrs
486million – 5 yrs
1.3 billion (Rs)
27 million
2? billion
120million – 5 yrs
17
Open-Source Grid Middleware Projects
18
Driving Theme:Community Grids vs. Utility Grids
TypeFeature
Community Grids Utility Grids
User QoS Best effort Contract/SLA
Service Pricing
Not considered /
free access
Usage, QoS level, Market supply and demand
Example Middleware
Globus, Condor, OMII, Unicore
Nimrod-G, Gridbus, & many inspired efforts
19
The Gridbus Project @ Melbourne:Enable Leasing of ICT Services on Demand
WWG
Pushes Grid computing into mainstream
computing
Gridbus
20
The Gridbus Project @ GRIDS Lab, The University of Melbourne: Toolkit for Creating and Deploying e-Research Applications on Utility Grids
The Gridbus Project @ GRIDS Lab, The University of Melbourne: The Gridbus Project @ GRIDS Lab, The University of Melbourne: Toolkit for Creating and Deploying eToolkit for Creating and Deploying e--Research Applications on Utility GridsResearch Applications on Utility Grids
Gridbus
Distributed Data
http://www.gridbus.org
• Gridbus is a “open source” Grid R&D project with focus on Grid Economy, Utility Grids and Service Oriented Computing.
• Gridbus Middleware components include:– Alchemi: .NET-based Enterprise Grid
– Grid Market Directory and Web Services
– Grid Bank: Accounting and Transaction Management
– Visual Tools for Creation of Distributed Applications
– Grid Service Broker and Scheduling
– Workflow Management Engine
– Libra: SLA-based Resource Allocation
– GridSim Toolkit
29
Agenda
Introduction Utility Networks and Grid Computing Application Drivers and Various Types of Grid Services
Global Grids and Challenges Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Market-based Management and Gridbus Software Stack
Grid Workflows and QoS Scheduling Architecture, Design and Implementation Performance Evaluation: Simulation based workflows
SLA-based Resource Allocation Utility based allocation, pricing, performance results
Summary and Conclusion
30
Workflow-based Applications
Workflow applications Scientific and engineering
domains (e.g., biology, astronomy, chemistry)
Task execution is based on their control and data dependencies.
(900000)1
5
6
2 3 4
109
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12 13
15
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SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
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SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
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(300000)
(150000)
(300000)
1
5
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2 3 4
109
11
12 13
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7
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SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
(Protein annotation workflow:London e-Science Centre)
31
Workflow for VR-Based Respiratory Treatment Planning System
Surface Extraction (SJTU)
Grid Generation Experiment (CSIRO)
MR
I Sca
ns
VR Visualization
CFD Simulation Comparison
Virtual Treatment
32
Driving Theme:Community Grids vs. Utility Grids
TypeFeature
Community Grids Utility Grids
User QoS Best effort Contract/SLA
Service Pricing
Not considered /
free access
Usage, QoS level, Market supply and demand
Example Workflow Systems
Triana, MyGrid, Askalon, DAGMan, Pegasus, GrADS Kepler
Gridbus Grid Workflow Engine
33
Workflow Scheduling
Scheduling on Community Grids Minimize the execution time based on best effort (ignores factors such
as monetary cost of resource access and various users’ QoS satisfaction levels.)
Scheduling on Utility Grids Focuses on mapping workflow tasks on services to satisfy users’ QoS
constraints (e.g. deadline, the quality of produced data). Supports negotiation and establishment of SLA as a contract between
users and providers Optimize performance under most important QoS constraints imposed
by users. Minimize execution cost while meeting a specified deadline. Minimize execution time while meeting a specified budget.
Support SLA-based allocation of resources so that multiple competing demands from users can be managed with the aim of enhancing providers profit.
34
Cost-based Workflow Scheduling
Objective Function
Minimize the execution cost and yet meet the time constraints imposed by users.
……
4, 100
10, 20
1, 200
Time, price
task
……
35
Workflow Management Systems
Support composition, deployment, and execution management of workflow applications:
Workflow language Graphical environment for workflow composition and monitor Grid middleware integration Data management Fault-tolerance QoS-based SLA negotiation Scheduling ...
Figure 2. Elements of a Grid Workflow Management System.
Workflow Design
Information Retrieval
Workflow Scheduling
Fault Tolerance
Data Movement
Grid Workflow Management System
36
Grid Workflow Application Modeling & Definition Tools
Grid Workflow Specification and Verification
Grid Workflow Management System
Resource Info Service
Application Catalogue
Build Time
Run Time
Workflow Design & Monitoring
Workflow Execution Control & Monitoring
Interaction with Grid resources
Interaction with VO Info services
QoS-based Workflow Scheduling
Fault Management Data ManagementData Catalogue
Virtual Organization
R2 Rn……
SLA-based Resource Allocation System(Plug-in for Existing Local Resource Managers)
Negotiation Services
ExecutionMonitor
E-Researchers/Users
feedback
Core Grid Services
SynchrotronData source
Global Grid
37
Architecture
GSP
Workflow Planning
Workflow Execution
Workflow Management System
Grid Service
Grid Service
Grid Service
Grid Service
Grid MarketDirectory
marketplace
Service Discovery
Advance Reservation
ServiceRequest(SLA)
contract violation
ReservationRequest(SLA)
Workflow Scheduling
GSP: Grid Service Provider
Feedback
SLA: Service Level Agreement
Workflow Specification
Performance Estimator
QoS Monitor
Executor
QoSRequest
38
Methodology
Discover available services and estimate execution time for every task.
Group workflow tasks into task partitions. Distribute users’ overall deadline into
every task partition. Query available time slots, generate
optimized schedule for each task partition and make advance reservations.
Start workflow execution and reschedule when the initial schedule is violated at run-time.
39
Predicting Execution Time
Reservation-enabled Utility Services Resource services
Provide proportions of hardware resources (e.g. computing processors, network bandwidth, storage, memory) as a service for remote client access.
Simulation, analytical modeling, empirical and historical data. Application services
Allow remote clients to use their specialized applications. Provide estimated service times based on the metadata of user’s
service requests.
40
Workflow Task Partitioning
Simple taskSynchronization task
T6
T7
T14
T5
T10
T8
T2
T9
T3
T4
T11
T12
T13
T1
T6
T7
T14
T5
T10
T8
T2
T9
T3 T4
T11
T12
T13
Branch
Before partitioning. After partitioning.
T1
41
Deadline Assignment/Distribution
P1. Any assigned sub-deadline must be greater than or equal to the minimum processing time of the corresponding task partition.
P2. The overall deadline is divided over task partitions in proportion to their minimum processing time.
P3. The cumulative sub-deadline of any independent path between two synchronization tasks must be same.
P4. The cumulative sub-deadline of any path from entry task to exit task is equal to the overall deadline.
350
(43)
(152)
(217)
(284)
(350)(53)
(187)(187)
(120)
(187)
(269)
(350)(269)
(253)
350
42
Planning
Generates an optimized schedule for advanced reservation and run-time execution.
Solve the problem based on divide-and-conquer.
Generate a optimized schedule for each partition based on its assigned sub-deadline.
A local optimized schedule minimizes execution cost while meeting its assigned sub-deadline.
A optimized schedule constructed by local schedules. Task partition optimization
Synchronization Task Scheduling Branch Task Scheduling
350
43
Task Partition Scheduling
Synchronization task scheduling Only one task. Solution: select the cheapest service that can
process the task and transfer data within the assigned sub-deadline.
Branch task scheduling One simple task in a branch. Multiple tasks in a branch.
Model a branch as a Markov Decision Process (MDP)
T1 T2 T3
44
Experiments
Different Workflow Structures
2
1
3
4
(300000)
(600000)
(900000)
(150000)
A
B
C
B
2
1
3
4
(300000)
(600000)
(900000)
(150000)
2
1
3
4
2
1
3
4
(300000)
(600000)
(900000)
(150000)
A
B
C
B
1 3 5 7
2 4 6 8
10 11 12
13 14 15
Align_wap
reslice
softmean
slicer
convert
(300000)
9
(600000)
(300000)
(600000)
(300000)
Align_wap
reslice
Align_wap Align_wap
reslice reslice
slicer slicer
convert convert
(300000) (300000) (300000)
(600000) (600000) (600000)
(300000) (300000)
(600000) (600000)
1 3 5 7
2 4 6 8
10 11 12
13 14 15
Align_wap
reslice
softmean
slicer
convert
(300000)
9
(600000)
(300000)
(600000)
(300000)
Align_wap
reslice
Align_wap Align_wap
reslice reslice
slicer slicer
convert convert
(300000) (300000) (300000)
(600000) (600000) (600000)
(300000) (300000)
(600000) (600000)
(900000)1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
(900000)1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
Pipeline Parallel Hybrid structure
(fMRI’s neuroscience workflow)
(Protein annotation workflow:London e-Science Centre)
45
(Simulation) Experiments
MI (million instructions) represents length of tasks MIPS (Million Instructions per Second) represents the
processing capability of services. Service type represents different types of services. 15 types of services, each supported by 10 different service
providers with different processing capability.
ServiceID
Processing Time(sec)
Cost (G$)
1 1200 300
2 600 600
3 400 900
4 300 1200
Bandwidth(Mbps)
Cost/sec (G$/sec)
100 1
200 2
512 5.12
1024 10.24
Table I. Service speed andcorresponding price for executing a task.
Table II. Transmission bandwidth and corresponding price.
46
Experiments
Compared heuristics Greedy cost
sorts services by their prices. assigns as many tasks as possible to cheapest services without
exceeding the deadline. Deadline-level
divides workflow tasks into levels based on their depth in the workflow graph.
assigns sub-deadlines to each task level equally.
47
Results
Pipeline Application
0
0.5
1
1.5
2
2.5
0.5 1 1.5 2 2.5
User Deadline (hours)
Co
mp
leti
on
Tim
e (
ho
urs
)
Deadline-MDP
Deadline-Level
Greedy-Cost
Pipeline Application
0
5000
10000
15000
20000
25000
30000
0.5 1 1.5 2 2.5
User Deadline (hours)
Execu
tio
n C
ost
(G$)
Deadline-MDP
Deadline-Level
Greedy-Cost
Parallel Application
0
0.5
1
1.5
2
2.5
0.5 1 1.5 2 2.5
User Deadline (hours)
Co
mp
leti
on
Tim
e (
ho
urs
)
Deadline-MDP
Deadline-Level
Greedy-Cost
Parallel Application
0
2000
4000
6000
800010000
12000
14000
16000
18000
0.5 1 1.5 2 2.5
User Deadline (hours)E
xecu
tio
n C
ost
(G$)
Deadline-MDP
Deadline-Level
Greedy-Cost
48
Results
Hybrid Structure Application
0
0.5
1
1.5
2
2.5
3
0.5 1 1.5 2 2.5
User Deadline (hours)
Co
mp
leti
on
Tim
e (
ho
urs
)
Deadline-MDP
Deadline-Level
Greedy-Cost
Hybrid Structure Application
0
2000
4000
6000
8000
10000
12000
0.5 1 1.5 2 2.5
User Deadline (hours)
Exe
cuti
on
Co
st (
G$)
Deadline-MDP
Deadline-Level
Greedy-Cost
49
Agenda
Introduction Utility Networks and Grid Computing Application Drivers and Various Types of Grid Services
Global Grids and Challenges Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Market-based Management and Gridbus Software Stack
Grid Workflows and QoS Scheduling Architecture, Design and Implementation Performance Evaluation: Simulation based workflows
SLA-based Resource Allocation Utility based allocation, pricing, performance results
Summary and Conclusion
50
Utility-driven Cluster RMS Architecture for GSPs
51
Economy-based Admission Control & Resource Allocation
Uses the pricing function to compute cost for satisfying the QoS of a job as a means for admission control Regulate submission of workload into the cluster to
prevent overloading Provide incentives
Deadline -- $ Execution Time -- $ Cluster Workload -- $
Cost acts as a mean of feedback for user to respond to
52
Impact of Penalty Function on Utility
53
Normalised Comparison of FCFS, Libra & Libra+$
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Job QoSSatisfaction
Cluster Profitability Average WaitingTime
Average ResponseTime
FCFS
Libra
Libra+$, β = 0.01
54
Impact of Increasing Dynamic Pricing Factor on GSP Profitability
0%
10%
20%
30%
40%
50%
60%
70%
80%
0.01 0.1 0.3 1
Dynamic Pricing Factor β
Clu
ster
Pro
fita
bili
ty (
%)
FCFS
Libra
Libra+$
55
Agenda
Introduction Utility Networks and Grid Computing Application Drivers and Various Types of Grid Services
Global Grids and Challenges Security, resource management, pricing models, …
Service-Oriented Grid Architecture and Gridbus Market-based Management and Gridbus Software Stack
Grid Workflows and QoS Scheduling Architecture, Design and Implementation Performance Evaluation: Simulation based workflows
SLA-based Resource Allocation Utility based allocation, pricing, performance results
Summary and Conclusion
56
Summary and Conclusion
Grids exploit synergies that result from cooperation of autonomous entities:
Resource sharing, dynamic provisioning, and aggregation at global level Great Science and Great Business!
Grids have emerged as enabler for Cyberinfrastructure that powers e-Science and e-Business applications.
SOA + Market-based Grid Management = Utility Grids Grids allow users to dynamically lease Grid services
at runtime based on their quality, cost, availability, and users QoS requirements.
Delivering ICT services as computing utilities. QoS Scheduling of Workflows and SLA-based resource
allocation enables ability of Grids to serve as IT backbone for delivering utility computing services.
57
Thanks for your attention!
We Welcome Cooperation in Research and Development!http:/www.gridbus.org
eScience2007.org
Backup
MDP etc.
59
Markov Decision Process (MDP)
Effective for solving sequential decision problems. A MDP model contains:
A set of possible system states A set of possible actions A real valued reward (penalty) function A transition of each action’s effects in each state
60
MDP Model
States A state consists of current execution task, ready time and
current location. Actions
An action in the MDP is to allocate a time slot on a service to a task.
t : input data transmission time plus the processing time of the service.
c: transmission cost plus the service cost.
61
Immediate penalty obtained from taking action a in state s and transitioning to state s’.
Expected penalty The sum of immediate penalties from current
state to a terminal state.
The optimal action for state s is:
MDP Model
)}'(),({min)( sUasusUsAa
)(s,a,s'u =
, otherwisea.c
, sub-deadlineRTs'.
)}'(),({minarg)(* sUasussAa
Expected penalty
62
Implementation
Value iteration is a standard dynamic programming algorithm compute a new value function for each state based on the
current value of its next state. value iteration proceeds in an iterative fashion and can
converge to the optimal solution quickly. record a number of candidate solutions while finding the
optimal time slot.
63
Rescheduling
Re-adjust sub-deadline and re-compute optimal schedules for unexecuted task partitions.
Reschedule minimum number of tasks.
(900000)1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
(900000)1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)
1
5
6
2 3 4
109
11
12 13
15
7
14
SignalP COILS2 SEG PROSITE
TMHMM
Prospero HMMer
PSI-BLAST BLAST IMPALA
Summary
PSI-PRED
3D-PSSM
Genome
Summary
SCOP
(300000) (600000) (600000)
(300000)
(150000)
8
(150000)
(300000) (300000) (300000)
(600000)
(600000)
(300000)
(150000)
(300000)