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 AvailabilityBest effortAdvanced Reservation QoSBest effortContract/SLA PricingNot 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 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.

Application Model A BC 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  Representation of individual in the population.  Determination of the fitness function.  Design of genetic operators.

Problem encoding

 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: 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.

Schedule refinement

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

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$) Bandwidth (Mbps) Cost/sec (G$/sec) 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 , 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

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