Grids are emerging as a promising solution for resource and computation demanding applications. Here, task scheduling is one of the most important subjects. In traditional approaches for grid scheduling, makespan or completion time of scheduling is the most important parameter for optimization. But in these algorithms, some parameters like user requirements aren't considered. Over the last few years, grid technologies have progressed towards a service-oriented paradigm that enables users to consume these services, based on their QoS (Quality of Service) requirements. However, the heterogeneity of resources in grid computing complicates resource management and scheduling of applications. In addition, the commercialization of the grid requires policies that can take into account user requirements, and budget considerations, in particular. In this paper a new list heuristic algorithm for workflow applications modeled as Directed Acyclic Graphs (DAGs), is developed to solve the scheduling problem, considering time and cost parameters. In this algorithm, the objective function is optimizing both time and cost using a greedy approach.
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