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Resource Allocation for Multiple Workflows in Cloud-Fog Computing Systems

Constant innovations in the Internet of Things (IoT) in latest years have generated large amounts of data, putting pressure on the infrastructure of cloud computing. Fog computing has recently become a popular computing paradigm that can provide computing resources close to the end users and solve multiple issues with the current cloud-only systems. However, the scheduling of workflow applications in the cloud-fog environment to find the best tradeoff between makespan and price is facing enormous challenges. To address such a challenge, this paper presents an adaptation of the Path-Clustering Heuristic to the cloud-fog environment for multiple workflows. Firstly, we define the models for workflow execution time and resource cost in fog computing.Afterwards, we describe the newly proposed algorithms. We validate the efficiency of the algorithms with extensive simulation. Experimental results show that our scheduling adaptation achieves better performance while keeping similar costs compared to others.