A PSO-Based Hierarchical Resource Scheduling Strategy on Cloud Computing

Cloud computing environments facilitate applications by providing virtualized resources that can be provisioned dynamically. Computing resources are delivered by Virtual Machines (VMs). In such a scenario, resource scheduling algorithms play an important role where the aim is to schedule applications effectively so as to reduce the turn-around time and improve resource utilization. In this paper, we present a Particle Swarm Optimization (PSO) based strategy schedules applications to cloud resource taking into account both transmission cost and current load. In addition, a novel inertia weight was introduced in order to get the global search and local search effectively and avoid plunging into the local optimum. Finally, we experiment with application workflows by varying its performance and convergence analysis.

[1]  Angel Eduardo Muñoz Zavala,et al.  Constrained optimization with an improved particle swarm optimization algorithm , 2008, Int. J. Intell. Comput. Cybern..

[2]  Ying Wang,et al.  A dynamic priority scheduling algorithm on service request scheduling in cloud computing , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[3]  Alexandru Iosup,et al.  Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing , 2011, IEEE Transactions on Parallel and Distributed Systems.

[4]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[5]  Xiao Liu,et al.  A market-oriented hierarchical scheduling strategy in cloud workflow systems , 2011, The Journal of Supercomputing.