A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing

Cloud computing has gained popularity in recent times. As a cloud must provide services to many users at the same time and different users have different QoS requirements, the scheduling strategy should be developed for multiple workflows with different QoS requirements. In this paper, we introduce a Multiple QoS Constrained Scheduling Strategy of Multi-Workflows (MQMW) to address this problem. The strategy can schedule multiple workflows which are started at any time and the QoS requirements are taken into account. Experimentation shows that our strategy is able to increase the scheduling success rate significantly.

[1]  Geoffrey Fox,et al.  Special Issue: Workflow in Grid Systems , 2006, Concurr. Comput. Pract. Exp..

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

[3]  Elisa Heymann,et al.  Analysis of Dynamic Heuristics for Workflow Scheduling on Grid Systems , 2006, 2006 Fifth International Symposium on Parallel and Distributed Computing.

[4]  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..

[5]  Rizos Sakellariou,et al.  Scheduling multiple DAGs onto heterogeneous systems , 2006, Proceedings 20th IEEE International Parallel & Distributed Processing Symposium.

[6]  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).

[7]  Hai Jin,et al.  A throughput maximization strategy for scheduling transaction‐intensive workflows on SwinDeW‐G , 2008, Concurr. Comput. Pract. Exp..

[8]  R. Buyya,et al.  A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms , 2006, 2006 Workshop on Workflows in Support of Large-Scale Science.

[9]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[10]  Rajkumar Buyya,et al.  A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Global Grids , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[11]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[12]  Rizos Sakellariou,et al.  A hybrid heuristic for DAG scheduling on heterogeneous systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..

[13]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[14]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[15]  Weisong Shi,et al.  A Planner-Guided Scheduling Strategy for Multiple Workflow Applications , 2008, 2008 International Conference on Parallel Processing - Workshops.

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

[17]  Weisong Shi,et al.  An Adaptive Rescheduling Strategy for Grid Workflow Applications , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[18]  Geoffrey C. Fox,et al.  Workflow in Grid Systems , 2004 .

[19]  Rajkumar Buyya,et al.  Workflow scheduling algorithms for grid computing , 2008 .