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
LaboratoryDept. of Computer Science and Software
EngineeringThe University of Melbourne,
Australia
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Content

• Introduction
• Utility Grids
• Problem overview
• Genetic Algorithms
• Proposed Work
• Experiment Results
• Related work
• Conclusion and future work

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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.

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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
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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.

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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.

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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.

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Proposed Work

• Existing GAs
• Schedule dependent tasks in homogeneous
  multiprocessor systems.
• Minimize execution time or maximize system
  throughput.
• Our work
• Schedule dependent tasks in heterogeneous
  environments.
• Minimize execution time while meeting users
  budget.

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Application Model

• There is no cycle in the graph.
• A task cannot be executed until all of its parent
  tasks are completed.

A
B
C
D
Directed Acyclic Graph (DAG)
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Construction of a Genetic Algorithm

• Representation of individual in the population.
• Determination of the fitness function.
• Design of genetic operators.

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Problem encoding
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Fitness function

• 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

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Genetic operators

• Selection
• Retain fittest individuals in the population as
  successive generations evolve.
• Crossover
• Produce new individuals by combining the two
  existing individuals.
• Mutation

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Crossover
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Mutation Operations

• Mutation operations
• Allow 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.

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Schedule refinement
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Experiments

• GridSim experiment environment

1.register(service type)
GIS
2. query(type A)
Grid Service
3.service list
1. register
Workflow System
4. AvailableSlotQuery(duration)
5. slots
Grid Service
6. makeReservation(task )
GIS Grid Index System
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Experiments

• Applications

Unbalanced structure
Balanced structure
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Experiments

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

Table I. Service speed and corresponding price
for executing a task.
Table II. Transmission bandwidth and
corresponding price.
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
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Evolution of execution time and cost during 100
generations.
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Evolution of execution time and cost in response
to different refinement rate when budget is
G3000.
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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.

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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.

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Conclusion and Future Work

• Budget constrained workflow scheduling
• Minimize execution time while meeting users
  budget
• Genetic algorithms
• Fitness function
• Crossover and Mutation
• Future work
• Different negotiation models
• Run time rescheduling
• Other QoS constraints

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