One of the main challenges in Grid computing is efficient allocation of resources (CPU-hours, network bandwidth) to the tasks submitted by users. In our previous work a technique to allocate resources in a grid environment using predicted data has been proposed. We propose utilization of the predicted data the resources were classified into three types; they are permanent resources, semi-permanent and sporadic resources. These types of resources may become available for a time that is either higher than the dwelling time or lower than the dwelling time in a grid environment. As the nature features are not known in such classification and then allocation mechanism, the performance cannot be increased further. In order to avoid such problem, in this study, a prediction model and an allocation factor are introduced. These parameters are determined for the sporadic type and semi-permanent type of resources and they are used in the fuzzy-based resource allocation mechanism. The incorporation of these parameters in the resource allocation leads to a remarkable resource utilization rate and makespan. This can be observed from the simulation and comparative results. From the results, it can be said that the proposed resource allocation mechanism has proved the performance in a dynamic environment

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In this paper, we introduce the concepts of fuzzy upper and fuzzy lower almost e*-continuous multifunctions on fuzzy topological spaces in Ŝostak’s sense. Several characterizations and properties of these fuzzy upper (resp. fuzzy lower) almost e* -continuous multifunctions are presented and their mutual relationships are established in L-fuzzy topological spaces. Later, composition and union between these multifunctions have been studied.