As distributed computing infrastructures become nowadays ever more complex and heterogeneous, scientists are confronted with multiple competing goals such as makespan in high-performance computing and economic cost in Clouds. Existing approaches typically aim at finding a single tradeoff solution by aggregating or constraining the objectives in an a-priory fashion, which negatively impacts the quality of the solutions. In contrast, Pareto-based approaches aiming to approximate the complete set of (nearly-) optimal tradeoff solutions have been scarcely studied. In this paper, we extend the popular Heterogeneous Earliest Finish Time (HEFT) workflow scheduling heuristic for dealing with multiple conflicting objectives and approximating the Pareto frontier optimal schedules. We evaluate our new algorithm for performance and cost tradeoff optimisation of synthetic and real-world applications in Distributed Computing Infrastructures (DCIs) and federated Clouds and compare it with a state-of-the-art meta-heuristic from the multi-objective optimisation community.
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