Miloud Khaldi

• PhD
• Professor (Assistant) at Ecole Superieure en Informatique, Sidi Bel Abbes, Algeria

High computation power is required to execute complex scientific workflows. Cloud computing resources are used viably to perform such complex workflows. Task clustering has demonstrated to be an efficient technique to decrease system overhead and to enhance the fine computational granularity tasks of a scientific workflow executed on distributed re...

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, computer resources and network failures are no more exceptions, but belong to the normal system behavior. Fault tolerance in grid computing improves enormously the calculation time and gives...

Fault Tolerance for a Scientific Workflow System in a Cloud Computing Environment

Many scientific workflows are composed of fine computational granularity tasks, where the task runtime may be shorter than the system overhead—the period of time during which miscellaneous work other than the user’s computation is performed. Task clustering methods merge several short tasks into a single job such that the job runtime is increased a...

Computational grids have the potential for solving large-scale scientific problems using heterogeneous and geographically distributed resources. At this scale, the characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. In this paper, we propose FT-GRC a fault tolerance model that seeks to find...

Fault tolerance in grid computing improves enormously the calculation time and gives more confidence to the users of grids. The characteristics of dynamicity, resource heterogeneity and scalability have made fault tolerance more complex. We propose, in this paper, a fault tolerance model that seeks to find the most suitable substitute for the faile...

The extraction of association rules in distributed systems is capable of greatly reducing the time of extraction and exploitation of large data sets, however, these benefits are forced to emerging issues related on the one hand the nature of distributed systems and the sharp increase in volumes of data and its geographic dispersion. The trend towar...