A dynamic resource allocation and task scheduling strategy with uncertain task runtime on IaaS clouds

Performing scientific workflows on IaaS cloud may face the problem of uncertain task runtime, meaning that tasks' real execution time are different from their given or evaluated time. So the pre-determined scheduling scheme can not perform as expected, causing that scientific workflows can not be completed within deadline or the total cost is far beyond users' budget. To address this problem, we proposed a new dynamic resource allocation and task scheduling(DRATS) strategy. At build-time, we use Path cut(PC) algorithm to generate a static task-Virtual Machine(VM) mapping scheme, and use task duplication(TD) algorithm to reduce the affect caused by their potential uncertain task runtime; At running time, when any task's executing time is inaccurate, we use least resource appending(LAR) algorithm to re-organize the mapping relationship between successor tasks and VMs, and add new virtual machine as cheap as possible. Experimental results demonstrate that, DRATS strategy can decrease the impact brought by uncertain task runtime, improve the probability of completing scientific workflows on time, and reduce the execution cost of scientific workflows effectively while satisfying the deadline constraint.

[1]  Emmanuel Jeannot,et al.  Comparative Evaluation Of The Robustness Of DAG Scheduling Heuristics , 2008, CoreGRID Integration Workshop.

[2]  Radu Prodan,et al.  Dynamic Cloud provisioning for scientific Grid workflows , 2010, 2010 11th IEEE/ACM International Conference on Grid Computing.

[3]  Filip De Turck,et al.  Cost-aware scheduling of deadline-constrained task workflows in public cloud environments , 2013, 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013).

[4]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[5]  Radu Prodan,et al.  A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments , 2012, 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012).

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

[7]  Luiz Fernando Bittencourt,et al.  HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds , 2011, Journal of Internet Services and Applications.

[8]  Li-zhen Cui,et al.  A Multiple QoS Constrained Scheduling Strategy of Multiple Workflows for Cloud Computing , 2009, 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications.

[9]  Chase Qishi Wu,et al.  On Scientific Workflow Scheduling in Clouds under Budget Constraint , 2013, 2013 42nd International Conference on Parallel Processing.

[10]  Jarek Nabrzyski,et al.  Algorithms for cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds , 2015 .

[11]  Xiaorong Li,et al.  A sequential cooperative game theoretic approach to scheduling multiple large-scale applications in grids , 2014, Future Gener. Comput. Syst..

[12]  Rajkumar Buyya,et al.  Meeting Deadlines of Scientific Workflows in Public Clouds with Tasks Replication , 2014, IEEE Transactions on Parallel and Distributed Systems.

[13]  Albert Y. Zomaya,et al.  Rescheduling for reliable job completion with the support of clouds , 2010, Future Gener. Comput. Syst..

[14]  John K. Antonio,et al.  Cost-Minimizing Scheduling of Workflows on a Cloud of Memory Managed Multicore Machines , 2009, CloudCom.

[15]  Jarek Nabrzyski,et al.  Cost- and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds , 2012, 2012 International Conference for High Performance Computing, Networking, Storage and Analysis.

[16]  Daniel A. Menascé,et al.  A framework for resource allocation in grid computing , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[17]  Shiyong Lu,et al.  Scheduling Scientific Workflows Elastically for Cloud Computing , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[18]  Xiaoping Li,et al.  Time-Cost Tradeoff Dynamic Scheduling Algorithm for Workflows in Grids , 2006, 2006 10th International Conference on Computer Supported Cooperative Work in Design.

[19]  Albert Y. Zomaya,et al.  Profit-driven scheduling for cloud services with data access awareness , 2012, J. Parallel Distributed Comput..

[20]  Mei-Hui Su,et al.  Characterization of scientific workflows , 2008, 2008 Third Workshop on Workflows in Support of Large-Scale Science.

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

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

[23]  Helen D. Karatza,et al.  Scheduling real-time DAGs in heterogeneous clusters by combining imprecise computations and bin packing techniques for the exploitation of schedule holes , 2012, Future Gener. Comput. Syst..

[24]  Jian Li,et al.  Cost-efficient task scheduling for executing large programs in the cloud , 2013, Parallel Comput..

[25]  Dick H. J. Epema,et al.  Deadline-constrained workflow scheduling algorithms for Infrastructure as a Service Clouds , 2013, Future Gener. Comput. Syst..

[26]  WenAn Tan,et al.  A Trust Service-Oriented Scheduling Model for Workflow Applications in Cloud Computing , 2014, IEEE Systems Journal.

[27]  Kenjiro Taura,et al.  Acceleration of Data-Intensive Workflow Applications by Using File Access History , 2012, 2012 SC Companion: High Performance Computing, Networking Storage and Analysis.

[28]  Dick H. J. Epema,et al.  Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths , 2012 .

[29]  Xiaoping Li,et al.  Deadline division-based heuristic for cost optimization in workflow scheduling , 2009, Inf. Sci..

[30]  Rajkumar Buyya,et al.  Adaptive workflow scheduling for dynamic grid and cloud computing environment , 2013, Concurr. Comput. Pract. Exp..