A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms

Over the last few years, grid technologies have progressed towards a service-oriented paradigm that enables a new way of service provisioning based on utility computing models. Users consume these services based on their QoS (quality of service) requirements. In such ldquopay-per-userdquo grids, workflow execution cost must be considered during scheduling based on users' QoS constraints. In this paper, we propose a budget constraint based scheduling, which minimizes execution time while meeting a specified budget for delivering results. A new type of genetic algorithm is developed to solve the scheduling optimization problem and we test the scheduling algorithm in a simulated grid testbed.

[1]  Warren Smith,et al.  Predicting Application Run Times Using Historical Information , 1998, JSSPP.

[2]  Lily B. Mummert,et al.  Using a utility computing framework to develop utility systems , 2004, IBM Syst. J..

[3]  Nirwan Ansari,et al.  A Genetic Algorithm for Multiprocessor Scheduling , 1994, IEEE Trans. Parallel Distributed Syst..

[4]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[5]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[6]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[7]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[8]  Radu Prodan,et al.  Dynamic scheduling of scientific workflow applications on the grid: a case study , 2005, SAC '05.

[9]  Yong Zhao,et al.  Grid middleware services for virtual data discovery, composition, and integration , 2004, MGC '04.

[10]  Ken Kennedy,et al.  TaskScheduling Strategies forWorkflow-based Applications inGrids , 2005 .

[11]  Rajkumar Buyya,et al.  Cost-based scheduling of scientific workflow applications on utility grids , 2005, First International Conference on e-Science and Grid Computing (e-Science'05).

[12]  Rainer Schmidt,et al.  VGE - a service-oriented grid environment for on-demand supercomputing , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[13]  Rajkumar Buyya,et al.  Grid Simulation Infrastructure Supporting Advance Reservation , 2004 .

[14]  David Abramson,et al.  An Evaluation of Economy-based Resource Trading and Scheduling on Computational Power Grids for Parameter Sweep Applications , 2000 .

[15]  Radu Prodan,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005, SGMD.

[16]  Adam Arbree,et al.  Mapping Abstract Complex Workflows onto Grid Environments , 2003, Journal of Grid Computing.

[17]  Radu Prodan,et al.  ASKALON: a tool set for cluster and Grid computing , 2005, Concurr. Pract. Exp..

[18]  Rajkumar Buyya,et al.  A Taxonomy of Workflow Management Systems for Grid Computing , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[19]  Xingfu Wu,et al.  Using Performance Prediction to Allocate Grid Resources , 2004 .

[20]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..

[21]  S E Middleton,et al.  GEMSS: grid-infrastructure for medical service provision. , 2005, Methods of information in medicine.

[22]  Albert Y. Zomaya,et al.  Observations on Using Genetic Algorithms for Dynamic Load-Balancing , 2001, IEEE Trans. Parallel Distributed Syst..

[23]  Mauricio G. C. Resende,et al.  Greedy Randomized Adaptive Search Procedures , 1995, J. Glob. Optim..

[24]  Rizos Sakellariou,et al.  A low-cost rescheduling policy for efficient mapping of workflows on grid systems , 2004, Sci. Program..

[25]  Matthew R. Pocock,et al.  Taverna: a tool for the composition and enactment of bioinformatics workflows , 2004, Bioinform..

[26]  Joachim Geiler,et al.  Workflow-based Grid applications , 2006, Future Gener. Comput. Syst..

[27]  Kuo-Chi Lin,et al.  An incremental genetic algorithm approach to multiprocessor scheduling , 2004, IEEE Transactions on Parallel and Distributed Systems.

[28]  Ken Kennedy,et al.  Scheduling strategies for mapping application workflows onto the grid , 2005, HPDC-14. Proceedings. 14th IEEE International Symposium on High Performance Distributed Computing, 2005..

[29]  Marios D. Dikaiakos,et al.  Scheduling Workflows with Budget Constraints , 2007, Grid 2007.

[30]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[31]  Albert Y. Zomaya,et al.  Genetic Scheduling for Parallel Processor Systems: Comparative Studies and Performance Issues , 1999, IEEE Trans. Parallel Distributed Syst..

[32]  Nathalie Furmento,et al.  ICENI Dataflow and Workflow: Composition and Scheduling in Space and Time , 2003 .

[33]  Francine Berman,et al.  New Grid Scheduling and Rescheduling Methods in the GrADS Project , 2004, IPDPS Next Generation Software Program - NSFNGS - PI Workshop.

[34]  Andreas Geppert,et al.  Market-Based Workflow Management , 1998, Int. J. Cooperative Inf. Syst..

[35]  Henri Casanova,et al.  Grid Workflow Software for a High-Throughput Proteome Annotation Pipeline , 2004, LSGRID.

[36]  Graham R. Nudd,et al.  Pace—A Toolset for the Performance Prediction of Parallel and Distributed Systems , 2000, Int. J. High Perform. Comput. Appl..

[37]  I. Foster,et al.  The Physiology of the Grid , 2003 .

[38]  John Darlington,et al.  Mapping of Scientific Workflow within the e-Protein project to Distributed Resources , 2004 .