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 o...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.展开更多
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 th...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.展开更多
基金Project supported by the National Natural Science Foundation of China (No. 90412014), the National Science Foundation of China for Distinguished Young Scholars (No. 60225009), and the China Next Generation Internet (CNGI) Project (No. CNGI-04-15-7A)
文摘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.
基金Project supported by the National Natural Science Foundation of China (No. 60225009), and the National Science Fund for Distin-guished Young Scholars, China
文摘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.
