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In recent years, edge computing has become the ideal computing paradigm for various smart systems, such as smart logistics, smart health and smart transportation. This is due to its advantages including fast response times, energy efficiency and cost effectiveness over conventional cloud computing platforms. However, running complex computational scientific workflow tasks is still a very challenging issue at the edge, due to its typical three-layered computing environment consisting of an end device layer, an edge server layer, and a cloud server layer. A large number of recent studies have proposed different solutions for optimizing such computing resource management problems in an edge computing environment. However, since evaluation of most such studies is conducted through simulation, the effectiveness cannot be guaranteed in a real world environment. Therefore, to advance research on efficient …