A Budget Constrained Scheduling of Workflow Applications on Utility Grids using Genetic Algorithms

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A Budget Constrained Scheduling of
Workflow Applications on Utility Grids
using Genetic Algorithms

Jia Yu and Rajkumar Buyya



Grid Computing and Distributed Systems Laboratory

Dept. of Computer Science and Software Engineering

The University of Melbourne, Australia

Content


Introduction


Utility Grids


Problem overview


Genetic Algorithms


Proposed Work


Experiment Results


Related work


Conclusion and future work

Utility Computing and Utility Grids


Utility Computing


New service provisioning model.


Providing computing services such as servers,
storage and applications.


Pay
-
per
-
use.


Utility Grids


Grid computing provides a global infrastructure for
resource sharing and integration.


Enabling users to consume utility services
transparently over a secure, shared, scalable and
standard world
-
wide network environment.








Community Grids vs. Utility Grids

Community
Grids

Utility Grids

Availability

Best effort

Advanced
Reservation

QoS

Best effort

Contract/SLA

Pricing

Not considered /

free access

Usage, QoS
level, Market
supply and
demand



Workflow Scheduling


Scheduling on Community Grids


Minimize the execution time ignoring other
factors such as monetary cost of resource access
and various users’ QoS satisfaction levels.


Scheduling on Utility Grids


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.

Genetic Algorithms


Random search method based on the principle of
evolution.


Exploitation of best solutions from past searches.


Exploration of new regions of the solution space.


A high
-
quality solution to be derived from a
large search space.


Genetic Algorithms


Each individual in the search
space of the problem represents
a solution.



A GA maintains a population
of individuals that evolves over
generations.



The quality of an individual is
determined by a fitness
function.





Proposed Work


Existing GAs


Schedu
le

dependent tasks in homogeneous
multiprocessor systems.


Minimize execution time or maximize system
throughput.


Our work


Schedul
e

dependent tasks in heterogeneous
environments.


Minimize execution time while meeting users’
budget.


Application Model

A

B

C

D

Directed Acyclic Graph (DAG)



There is no cycle in the graph.


A task cannot be executed
until all of its parent tasks are
completed.


Construction of a Genetic Algorithm


R
epresentation of individual in the
population.


D
etermination of the fitness function.


D
esign of genetic operators.

Problem encoding

T

0

T

1

T

2

T

3

T

4

T

5

T

6

T

7

T

0

T

1

T

2

T

3

T

4

T

5

T

6

T

7

Workflow

S

1

S

2

S

3

S

4

time

Schedule

T

0

T

2

T

7

T

1

T

3

T

5

T

4

T

6

T0(1)

-

T2(1)

-

T7(1)

-

T1(2)

-

T3(3)

-

T5(3)

-

T4(4)

-

T6(4)

S

1

:T

0

-

T

2

-

T

7

S

2

:T

1

S

3

:T

3

-

T

5

S

4

:T

4

-

T

6

Two

-

dimensional strings

One

-

dimensional string


Cost
-
fitness: encourages the formation of the solutions that
achieve the budget constraint.






c
(
I
) is the sum of the task execution cost and data transmission cost of
I

,
and
B

is the budget of the workflow.



Time
-
fitness: encourages the GA to choose individuals with
earliest completion time in the current population.






where
t
(
I
) is the completion time of
I

and
maxTime
is the largest
completion time of the current population.




Fitness function




Fitness function

Genetic operators


Selection


Retain fittest individuals in the population as
successive generations evolve.


Crossover


Produce new individuals by combining the two
existing individuals.


Mutation

Crossover

Mutation Operations


Mutation operations:


A
llow a certain
offspring
to obtain features that are
not possessed by either parent.


Swapping mutation


Swapping mutation aims to change the execution
order of tasks in an individual that compete for a
same time slot.


Replacing mutation


Replacing mutation aims to re
-
allocate an
alternative service to a task in an individual.








Schedule refinement



Experiments


GridSim experiment environment


Workflow

System

GIS

Grid
Service

1.register(service type
)

1. register

Grid
Service

2. query(type A)

3.service list

GIS
: Grid Index System

Experiments


Applications

Balanced structure


Unbalanced structure

Experiments


Service type represents different types of services.


15 types of services, each supported by 10 different
service providers with different processing capability.



Service

ID

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 and

corresponding price for executing a task.


Table II. Transmission

bandwidth and corresponding price.

Evolution of execution time and cost during 100 generations.

Evolution of execution time and cost in response to different refinement

rate when budget is G$3000.


Heuristics compared


Greedy time


Assigns a planed budget to each task in the workflow
based on the average estimated execution costs of tasks
and the total budget of the workflow.



Assigns each task to a service which can complete at
earliest time within its assigned sub
-
budget.



Related Work


Time optimization algorithms


Min
-
Min: vGrADS, Pegasus


HEFT: ASKLON


GRASP: Pegasus


Simulated Annealing: ICENI


Genetic Algorithms: ASKALON


Genetic algorithms in multiprocessors systems


Heuristics


E. Tsiakkouri et al., “Scheduling Workflows with Budget
Constraints”,
the CoreGRID Workshop on Integrated
Research in Grid Computing,
Nov. 28
-
30, 2005.

Conclusion and Future Work


Budget constrained workflow scheduling


Minimize execution time while meeting user’s budget


Genetic algorithms


Fitness function


Crossover and Mutation



Future work


Different negotiation models


Run time rescheduling


Other QoS constraints


Thank You… Any
??