High Performance Fault Tolerant Resource Scheduling in Computational Grid Environment

High Performance Fault Tolerant Resource Scheduling in Computational Grid Environment

Sukalyan Goswami (Institute of Engineering & Management, Kolkata, India) and Kuntal Mukherjee (Birla Institute of Technology, Mesra, Lalpur Campus, Ranchi, India)
DOI: 10.4018/IJWLTT.2020010104
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Virtual resources team up to create a computational grid, which is used in computation-intensive problem solving. A majority of these problems require high performance resources to compute and generate results, making grid computation another type of high performance computing. The optimization in computational grids relates to resource utilization which in turn is achieved by the proper distribution of loads among participating resources. This research takes up an adaptive resource ranking approach, and improves the effectiveness of NDFS algorithm by scheduling jobs in those ranked resources, thereby increasing the number of job deadlines met and service quality agreements met. Moreover, resource failure is taken care of by introducing a partial backup approach. The benchmark codes of Fast Fourier Transform and Matrix Multiplication are executed in a real test bed of a computational grid, set up by Globus Toolkit 5.2 for the justification of propositions made in this article.
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There are many challenges associated with computational grid, like, scheduling of jobs in appropriate resources for subsequent execution, increasing the number of deadline meets for submitted jobs, balancing of workload in grid and fault tolerance. Important related works in this arena of research are summarized in this section.

The load balancing process must take into consideration the dynamic loads of participating resources of the grid (Abo Rizka & Rekaby, 2012). Job scheduling has to ensure higher number of deadlines meets, thereby improving the performance of the grid. In (Goswami & Das, 2016), an adaptive execution scheme has been proposed which ensures guaranteed performance with respect to service quality agreements.

The properties of general distributed system is quite different from that of computational grid environment. Moreover, the client-server framework of grid proposed by Michael Stal (Stal, 1995) does not solve the problem of load balancing among participating resources in grid. Various job scheduling schemes in grid have been described in (Kant Soni, Sharma & Kumar Mishra, 2010). X-Dimension binary tree data model (Abo Rizka & Rekaby, 2012) have limited success in workload balancing among grid resources.

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