  Distributed computing, such as cloud computing, provides promising platforms
to execute multiple workflows. Workflow scheduling plays an important role in
multi-workflow execution with multi-objective requirements. Although there
exist many multi-objective scheduling algorithms, they focus mainly on
optimizing makespan and cost for a single workflow. There is a limited research
on multi-objective optimization for multi-workflow scheduling. Considering
multi-workflow scheduling, there is an additional key objective to maintain the
fairness of workflows using the resources. To address such issues, this paper
first defines a new multi-objective optimization model based on makespan, cost,
and fairness, and then proposes a global clustering-based multi-workflow
scheduling strategy for resource allocation. Experimental results show that the
proposed approach performs better than the compared algorithms without
significant compromise of the overall makespan and cost as well as individual
fairness, which can guide the simulation workflow scheduling on clouds.
