Abstract
Recently, computational Grids have proven to be a good solution for processing large-scale, computation intensive problems. However, the heterogeneity, dynamics of resources and diversity of applications requirements have always been important factors affecting their performance. In response to these challenges, this work first builds a Grid job scheduling architecture that can dynamically monitor Grid computing center resources and make corresponding scheduling decisions. Second, a Grid job model is proposed to describe the application requirements. Third, this paper studies the characteristics of commercial interconnection networks used in Grids and forecast job transmission time. Fourth, this paper proposes an application-aware job scheduling mechanism (AJSM) that includes periodic scheduling flow and a heuristic application-aware deadline constraint job scheduling algorithm. The rigorous performance evaluation results clearly demonstrate that the proposed application-aware job scheduling mechanism can successful schedule more Grid jobs than the existing algorithms. For successful scheduled jobs, our proposed AJSM method is the best algorithm for job average processing time and makespan.
Highlights
Computational Grids are a platform that can share, select, and aggregate geographically distributed heterogeneous idle computing resources to achieve vast computation and storage capabilities [1]
This paper proposes an application-aware job scheduling mechanism (AJSM), which mainly consists of periodic scheduling flow and a heuristic job scheduling algorithm
This is mainly due to the fact that our proposed ASJM strategy adopts two key techniques: job transmission time prediction based on the ARIMA model and heterogeneous Grid computing node resource normalization, which can give a more accurate job execution finish time
Summary
Computational Grids are a platform that can share, select, and aggregate geographically distributed heterogeneous idle computing resources to achieve vast computation and storage capabilities [1]. Automotive crash simulation analysis needs multidisciplinary finite element solver RADIOSS and pre-processing software HyperMesh, and CPU+GPU coordinated parallel computing [11, 12] These application requirements are not provided by all Grid computing centers. Grid application data transmission from the job submission point to the Grid computing center is a major challenge This is owing to the fact that most Grid systems are connected by commercial interconnect networks, the communication bandwidth of which is highly affected by the environments. These application-aware issues are worth further investigation for the job scheduling mechanism.
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