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

1. 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

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

3. 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.

4. Community Grids vs. Utility Grids

5. 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.

6. 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.

7. 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.

8. 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.

9. 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)

10. Construction of a Genetic Algorithm • Representation of individual in the population. • Determination of the fitness function. • Design of genetic operators.

11. Workflow Two - dimensional strings Schedule T T T T T T 0 0 1 1 2 2 S T T T 1 S :T - T - T 2 0 7 1 0 2 7 S :T T T T T S 2 1 3 3 4 4 T 2 1 S :T - T 3 3 5 S :T - T T T T T T T S 4 4 6 5 5 6 6 3 5 3 T T S 4 6 4 T T 7 7 time One - dimensional string T0(1) - T2(1) - T7(1) - T1(2) - T3(3) - T5(3) - T4(4) - T6(4) Problem encoding

12. 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

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

14. Crossover

15. 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.

16. Schedule refinement

17. 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

18. Experiments • Applications Unbalanced structure Balanced structure

19. 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.

20. Evolution of execution time and cost during 100 generations.

21. Evolution of execution time and cost in response to different refinement rate when budget is G$3000.

22. 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.

23. 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.

24. 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

25. Thank You… Any ??