Time-Constrained Workflow Scheduling In Cloud Environment Using Simulation

In cloud environment, it is necessary to find the efficient algorithms to optimize time-cost of cloud-oriented workflow scheduling. In this paper, a Simulated Annealing algorithm based heuristic is put forward in order to solve the timeconstrained scheduling problem. Time-cost of scheduling is consisted of tasks execution and data transmission. The simulation testing results demonstrates that, the time-cost of scheduling using this algorithm can cost less time compared with particle swarm optimization algorithm, SA-based scheduling can also balance the load on resources, and SA with good convergence can find global optimal solution faster.

[1]  Marta Mattoso,et al.  An adaptive parallel execution strategy for cloud‐based scientific workflows , 2012, Concurr. Comput. Pract. Exp..

[2]  Xiao-dong Zhang,et al.  Improved multi-objective particle swarm optimization algorithm for service-workflows scheduling , 2010, 2010 International Conference on Mechanic Automation and Control Engineering.

[3]  Ehl Emile Aarts,et al.  Simulated annealing and Boltzmann machines , 2003 .

[4]  Chase Qishi Wu,et al.  A cost-effective scheduling algorithm for scientific workflows in clouds , 2012, 2012 IEEE 31st International Performance Computing and Communications Conference (IPCCC).

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[6]  Manoj Kumar Tiwari,et al.  Constraint-based simulated annealing (CBSA) approach to solve the disassembly scheduling problem , 2012 .

[7]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

[8]  Omer F. Rana,et al.  Enforcing QoS in scientific workflow systems enacted over Cloud infrastructures , 2012, J. Comput. Syst. Sci..

[9]  Enda Barrett,et al.  A Learning Architecture for Scheduling Workflow Applications in the Cloud , 2011, 2011 IEEE Ninth European Conference on Web Services.

[10]  Kwang Mong Sim,et al.  Agent-based cloud workflow execution , 2012, Integr. Comput. Aided Eng..

[11]  Rajkumar Buyya,et al.  A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[12]  Chunlai Chai,et al.  Modeling Resource-Constrained Project Scheduling Problem and its Solution by Genetic Algorithm , 2013, J. Digit. Inf. Manag..

[13]  Amany M. Mohamed,et al.  Multi-objective Simulated Annealing Algorithm for Partner Selection in Virtual Enterprises , 2013, Artificial Intelligence, Evolutionary Computing and Metaheuristics.

[14]  A. Vasan,et al.  Comparative analysis of Simulated Annealing, Simulated Quenching and Genetic Algorithms for optimal reservoir operation , 2009, Appl. Soft Comput..

[15]  Ehl Emile Aarts,et al.  Simulated annealing and circuit layout , 1991 .

[16]  Mohammad Asif Zaman,et al.  Phased Array Synthesis Using Modified Particle Swarm Optimization , 2011 .