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T. D. Braun, H. J. Siegal, N. Beck, L. L. Boloni, M. Maheswaran, A. I. Reuther, J. P. Robertson, M. D. Theys, Y. Bin, D. Hensgen and R. F. Freund, “A Comparison Study of Static Mapping Heuristics for a Class of Meta-Tasks on Heterogeneous Computing Systems,” Proceedings ofthe 8th Heterogeneous Computing Workshop, San Juan, 12 April 1999, pp. 15-29.

has been cited by the following article:

• TITLE: Heuristic Scheduling Algorithms for Allocation of Virtualized Network and Computing Resources

  AUTHORS: Yichao Yang, Yanbo Zhou, Zhili Sun, Haitham Cruickshank

  KEYWORDS: Cloud Computing; Meta-Job Scheduling; Heuristic Algorithm; Load Balance; Network Virtualization

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.6 No.1, January 30, 2013

  ABSTRACT: Cloud computing technology facilitates computing-intensive applications by providing virtualized resources which can be dynamically provisioned. However, user’s requests are varied according to different applications’ computation ability needs. These applications can be presented as meta-job of user’s demand. The total processing time of these jobs may need data transmission time over the Internet as well as the completed time of jobs to execute on the virtual machine must be taken into account. In this paper, we presented V-heuristics scheduling algorithm for allocation of virtualized network and computing resources under user’s constraint which applied into a service-oriented resource broker for jobs scheduling. This scheduling algorithm takes into account both data transmission time and computation time that related to virtualized network and virtual machine. The simulation results are compared with three different types of heuristic algorithms under conventional network or virtual network conditions such as MCT, Min-Min and Max-Min. e evaluate these algorithms within a simulated cloud environment via an abilenenetwork topology which is real physical core network topology. These experimental results show that V-heuristic scheduling algorithm achieved significant performance gain for a variety of applications in terms of load balance, Makespan, average resource utilization and total processing time.