MAA: Multi-objective Artificial Algae Algorithm for Workflow Scheduling in Heterogeneous Fog-Cloud Environment

Abstract Cloud Computing (CC) is the most popular tool of choice for conducting scientific experimentation on Cloud Servers (CDs). It can be even more efficient strategy to use Fog Computing (FC) for allocating and executing operations on Fog Devices (FDs). Complex scientific operations need the effective use of virtual machines (VMs). Scientific workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service (QoS), interdependence between tasks, user deadlines, etc. There is a very less research available on scientific workflow scheduling in Fog-Cloud Environments (FCE). Classical scheduling techniques, evolutionary optimization algorithms, and other methodologies are the available solution to this problem. In this paper, an efficient meta-heuristic approach named Multi-objective Artificial Algae (MAA) algorithm is presented for scheduling scientific workflows in heterogeneous FCE. In the first phase, the algorithm preprocesses scientific workflow and prepares a tasks list. In order to speed up execution, bottleneck tasks are executed with high priority. The MAA algorithm is used to schedule tasks in the following stage to reduce execution times, energy consumption and costs. In order to effectively use fog resources, the algorithm also utilizes the weighted sum based objective function. The suggested approach is evaluated using five benchmark scientific workflows. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialized scheduling algorithms. In comparison to previous methodologies, the results demonstrate significant improvements in execution time, energy consumption and total cost without any trade-offs.