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Scheduling large-scale applications in heterogeneous Grid and Cloud systems is a fundamental NP-complete problem for obtaining good performance and execution costs. We address the problem of scheduling an important class of large-scale Grid applications inspired from real-world, characterised by a large number of homogeneous, concurrent, and computationally-intensive tasks that are the main sources of performance, cost, and storage bottlenecks. We propose a new formulation of this problem based on a cooperative distributed game theoretic method for makespan and cost optimisation of a multiple such applications while fulfilling important storage constraints. We present experimental results using simulation and real-world applications that demonstrate the effectiveness of our method in terms of the solution delivered, algorithm execution time, and fairness compared to other related approaches.