[Show abstract][Hide abstract] ABSTRACT: Job management is a key issue in computational grids, and normally involves job definition, scheduling, executing and monitoring.
However, job management in the existing grid middleware needs to be improved in terms of efficiency and flexibility. This
paper addresses a flexible architecture for job management with detailed design and implementation. Frameworks for job scheduling
and monitoring, as two important aspects, are also presented. The proposed job management has the advantages of reusability
of job definition, flexible and automatic file operation, visual steering of file transfer and job execution, and adaptive
application job scheduler. A job management wizard is designed to implement each step. Therefore, what the grid user needs
to do is only to define the job by constructing necessary information at runtime. In addition, the job space is adopted to
ensure the security of the job management. Experimental results showed that this approach is user-friendly and system efficient.
Journal of Zhejiang University - Science A: Applied Physics & Engineering 01/2007; 8(1):95-105. · 0.53 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Selecting appropriate resources for running a job efficiently is one of the common objectives in a computational grid. Resource
scheduling should consider the specific characteristics of the application, and decide the metrics to be used accordingly.
This paper presents a distributed resource scheduling framework mainly consisting of a job scheduler and a local scheduler.
In order to meet the requirements of different applications, we adopt HGSA, a Heuristic-based Greedy Scheduling Algorithm,
to schedule jobs in the grid, where the heuristic knowledge is the metric weights of the computing resources and the metric
workload impact factors. The metric weight is used to control the effect of the metric on the application. For different applications,
only metric weights and the metric workload impact factors need to be changed, while the scheduling algorithm remains the
same. Experimental results are presented to demonstrate the adaptability of the HGSA.
Journal of Zhejiang University - Science A: Applied Physics & Engineering 7(10):1634-1641. · 0.53 Impact Factor