Ordinal Optimized Scheduling of Scientific Workflows in Elastic Compute Clouds

Elastic compute clouds are best represented by the virtual clusters in Amazon EC2 or in IBM RC2. This paper proposes a simulation based approach to scheduling scientific workflows onto elastic clouds. Scheduling multitask workflows in virtual clusters is a NP-hard problem. Excessive simulations in months of time may be needed to produce the optimal schedule using Monte Carlo simulations. To reduce this scheduling overhead is necessary in real-time cloud computing. We present a new workflow scheduling method based on iterative ordinal optimization (IOO). This new method outperforms the Monte Carlo and Blind-Pick methods to yield higher performance against rapid workflow variations. For example, to execute 20,000 tasks on 128 virtual machines for gravitational wave analysis, an ordinal optimized schedule can be generated in a few minutes, which is O(103)~O(104) faster than using Monte Carlo simulations. The ordinal optimized schedule results in higher throughput with lower memory demand. The cloud experimental results being reported verified our theoretical findings on the relative performance of three workflow scheduling methods studied in this paper.

[1]  Alexandru Iosup,et al.  C-Meter: A Framework for Performance Analysis of Computing Clouds , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[2]  Yves Robert,et al.  Resource-aware allocation strategies for divisible loads on large-scale systems , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[3]  Y. Ho,et al.  Vector Ordinal Optimization , 2005 .

[4]  Stephen A. Jarvis,et al.  Performance-Aware Workflow Management for Grid Computing , 2005, Comput. J..

[5]  Cheng Wu,et al.  Fast Autotuning Configurations of Parameters in Distributed Computing Systems Using Ordinal Optimization , 2009, 2009 International Conference on Parallel Processing Workshops.

[6]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[7]  Subhash Saini,et al.  GridFlow: workflow management for grid computing , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[8]  Yu-Chi Ho,et al.  Ordinal optimization of DEDS , 1992, Discret. Event Dyn. Syst..

[9]  Radu Prodan,et al.  Performance and cost optimization for multiple large-scale grid workflow applications , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[10]  Dennis Gannon,et al.  Workflows for e-Science, Scientific Workflows for Grids , 2014 .

[11]  Douglas Thain,et al.  All-Pairs: An Abstraction for Data-Intensive Computing on Campus Grids , 2010, IEEE Transactions on Parallel and Distributed Systems.

[12]  R. F. Freund,et al.  Dynamic Mapping of a Class of Independent Tasks onto Heterogeneous Computing Systems , 1999, J. Parallel Distributed Comput..

[13]  Kai Lu,et al.  A Hybrid Policy for Job Scheduling and Load Balancing in Heterogeneous Computational Grids , 2007, Sixth International Symposium on Parallel and Distributed Computing (ISPDC'07).

[14]  John C. S. Lui,et al.  Bounding of Performance Measures for Threshold-Based Queuing Systems: Theory and Application to Dynamic Resource Management in Video-on-Demand Servers , 2002, IEEE Trans. Computers.

[15]  Francine Berman,et al.  A Slowdown Model for Applications Executing on Time-Shared Clusters of Workstations , 2001, IEEE Trans. Parallel Distributed Syst..

[16]  Loo Hay Lee,et al.  Constraint ordinal optimization , 2002, Inf. Sci..

[17]  Junwei Cao,et al.  A Case Study on the Use of Workflow Technologies for Scientific Analysis: Gravitational Wave Data Analysis , 2007, Workflows for e-Science, Scientific Workflows for Grids.

[18]  Atakan Dogan,et al.  Biobjective Scheduling Algorithms for Execution Time?Reliability Trade-off in Heterogeneous Computing Systems , 2005, Comput. J..

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

[20]  R. F. Freund,et al.  Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet , 1998, Proceedings Seventh Heterogeneous Computing Workshop (HCW'98).

[21]  Cheng Wu,et al.  Performance Optimization of Temporal Reasoning for Grid Workflows Using Relaxed Region Analysis , 2008, 22nd International Conference on Advanced Information Networking and Applications - Workshops (aina workshops 2008).

[22]  Yan Alexander Li,et al.  Minimizing the Application Execution Time Through Scheduling of Subtasks and Communication Traffic in a Heterogeneous Computing System , 1997, IEEE Trans. Parallel Distributed Syst..

[23]  Radu Prodan,et al.  Taxonomies of the Multi-Criteria Grid Workflow Scheduling Problem , 2008 .

[24]  Radu Prodan,et al.  Bi-Criteria Scheduling of Scientific Grid Workflows , 2010, IEEE Transactions on Automation Science and Engineering.

[25]  Albert Y. Zomaya,et al.  A Cooperative Game Framework for QoS Guided Job Allocation Schemes in Grids , 2008, IEEE Transactions on Computers.

[26]  Geoffrey C. Fox,et al.  High Performance Parallel Computing with Clouds and Cloud Technologies , 2009, CloudComp.

[27]  Geoffrey C. Fox,et al.  Hybrid cloud and cluster computing paradigms for life science applications , 2010, BMC Bioinformatics.

[28]  Kai Hwang,et al.  Adaptive Workload Prediction of Grid Performance in Confidence Windows , 2010, IEEE Transactions on Parallel and Distributed Systems.

[29]  Anthony A. Maciejewski,et al.  Robust Resource Allocation in Heterogeneous Parallel and Distributed Computing Systems , 2008, Wiley Encyclopedia of Computer Science and Engineering.

[30]  Qing-Shan Jia,et al.  Comparison of selection rules for ordinal optimization , 2006, Math. Comput. Model..

[31]  Joshua R. Smith,et al.  LIGO: the Laser Interferometer Gravitational-Wave Observatory , 1992, Science.

[32]  G. Bruce Berriman,et al.  On the Use of Cloud Computing for Scientific Workflows , 2008, 2008 IEEE Fourth International Conference on eScience.

[33]  Rajkumar Buyya,et al.  Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms , 2006, Sci. Program..

[34]  Rajkumar Buyya,et al.  Model-Driven Simulation of Grid Scheduling Strategies , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[35]  Loo Hay Lee,et al.  Multi-objective ordinal optimization for simulation optimization problems , 2007, Autom..

[36]  Peter A. Dinda,et al.  Size-based scheduling policies with inaccurate scheduling information , 2004, The IEEE Computer Society's 12th Annual International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems, 2004. (MASCOTS 2004). Proceedings..

[37]  Carl Kesselman,et al.  GriPhyN and LIGO, building a virtual data Grid for gravitational wave scientists , 2002, Proceedings 11th IEEE International Symposium on High Performance Distributed Computing.

[38]  Cheng Wu,et al.  AMREF: An Adaptive MapReduce Framework for Real Time Applications , 2010, 2010 Ninth International Conference on Grid and Cloud Computing.

[39]  Y. Ho,et al.  Ordinal Optimization: Soft Optimization for Hard Problems , 2007 .

[40]  Anthony A. Maciejewski,et al.  A stochastic model for robust resource allocation in heterogeneous parallel and distributed computing systems , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.