QoS-based Scheduling of e-Research Application Workflows on Global Grids Dr. Rajkumar Buyya Grid...

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







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


1.3 billion (Rs)

27 million

2? billion


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







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

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


TMHMM

Prospero HMMer


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


TMHMM

Prospero HMMer


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)

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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

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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

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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


TMHMM

Prospero HMMer


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


TMHMM

Prospero HMMer


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


TMHMM

Prospero HMMer


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)

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(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


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


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


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


Exe

cuti

on

Co

st (

G$)

Deadline-MDP

Deadline-Level

Greedy-Cost

49

Agenda







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







56


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.

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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


TMHMM

Prospero HMMer


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


TMHMM

Prospero HMMer


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


TMHMM

Prospero HMMer


Summary

PSI-PRED

3D-PSSM

Genome

Summary

SCOP

(300000) (600000) (600000)

(300000)

(150000)

8

(150000)

(300000) (300000) (300000)

(600000)

(600000)

(300000)

(150000)

(300000)

QoS-based Scheduling of e-Research Application Workflows on Global Grids Dr. Rajkumar Buyya Grid...

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