Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds

In this paper, a novel scheduling algorithm for Cloud-based workflow applications is presented. If the constituent workflow tasks are geographically distributed - hosted by different Cloud providers or data centers of the same provider - data transmission can be the main bottleneck. The algorithm therefore takes data dependencies between workflow steps into account and assigns them to Cloud resources based on the two conflicting objectives of cost and execution time according to the preferences of the user. Our implementation is based on BPEL, an industry standard for workflow modeling, and does not require any changes to the standard. It is based on, but not limited to, the Active BPEL engine and Amazon's Elastic Compute Cloud. To automatically adapt the scheduling decisions to network-related changes, the data transmission speed between the available resources is monitored continuously. Experimental results for a real-life workflow from a medical domain indicate that both the workflow execution times and the corresponding costs can be reduced significantly.

[1]  Cesare Alippi,et al.  Genetic-algorithm programming environments , 1994, Computer.

[2]  Radu Prodan,et al.  Grid Computing, Experiment Management, Tool Integration, and Scientific Workflows , 2007, Lecture Notes in Computer Science.

[3]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[4]  Randy H. Katz,et al.  Topology-aware resource allocation for data-intensive workloads , 2010, APSys '10.

[5]  Aravind Seshadri,et al.  A FAST ELITIST MULTIOBJECTIVE GENETIC ALGORITHM: NSGA-II , 2000 .

[6]  Jorge-Arnulfo Quiané-Ruiz,et al.  Runtime measurements in the cloud , 2010, Proc. VLDB Endow..

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[9]  Ladislau Bölöni,et al.  A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems , 2001, J. Parallel Distributed Comput..

[10]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[11]  Bernd Freisleben,et al.  Data Flow Driven Scheduling of BPEL Workflows Using Cloud Resources , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[12]  David Meredith,et al.  Evaluation of BPEL to Scientific Workflows , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[13]  David Corne,et al.  The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

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

[15]  Thomas Rauber,et al.  Load Balancing Concurrent BPEL Processes by Dynamic Selection of Web Service Endpoints , 2009, 2009 International Conference on Parallel Processing Workshops.

[16]  Liang Chen,et al.  Grid Service Orchestration Using the Business Process Execution Language (BPEL) , 2005, Journal of Grid Computing.

[17]  Hesham El-Rewini,et al.  Parallax: a tool for parallel program scheduling , 1993, IEEE Parallel & Distributed Technology: Systems & Applications.

[18]  Rajkumar Buyya,et al.  Multi-objective planning for workflow execution on Grids , 2007, 2007 8th IEEE/ACM International Conference on Grid Computing.

[19]  SiegelHoward Jay,et al.  Task Matching and Scheduling in Heterogeneous Computing Environments Using a Genetic-Algorithm-Based Approach , 1997 .

[20]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[21]  Bernd Freisleben,et al.  On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[22]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[23]  Tony Andrews.  Business Process Execution Language for Web Services Version 1.1 , 2003 .

[24]  Matjaz B. Juric,et al.  Business process execution language for web services , 2004 .

[25]  Rajkumar Buyya,et al.  Reliability-Oriented Genetic Algorithm for Workflow Applications Using Max-Min Strategy , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[26]  Hesham H. Ali,et al.  Task scheduling in parallel and distributed systems , 1994, Prentice Hall series in innovative technology.