MAA: multi-objective artificial algae algorithm for workflow scheduling in heterogeneous fog-cloud environment

Abstract

With the development of current computing technology, workflow applications have become more important in a variety of fields, including research, education, health care, and scientific experimentation. A group of tasks with complicated dependency relationships constitute the workflow applications. It can be difficult to create an acceptable execution sequence while maintaining precedence constraints. Workflow scheduling algorithms (WSA) are gaining more attention from researchers as a real-time concern. Even though a variety of research perspectives have been demonstrated for WSAs, it remains challenging to develop a single coherent algorithm that simultaneously meets a variety of criteria. There is very less research available on WSA in the heterogeneous computing system. Classical scheduling techniques, evolutionary optimisation algorithms, and other methodologies are the available solution to this problem. The workflow scheduling problem is regarded as NP-complete. This problem is constrained by various factors, such as Quality of Service, interdependence between tasks, and user deadlines. In this paper, an efficient meta-heuristic approach named Multi-objective Artificial Algae (MAA) algorithm is presented for scheduling scientific workflows in a hierarchical fog-cloud environment. In the first phase, the algorithm pre-processes scientific workflow and prepares two task lists. 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 overall costs. In order to effectively use fog resources, the algorithm also utilises the weighted sum-based multi-objective function. The proposed approach is evaluated using five benchmark scientific workflow datasets. To verify the performance, the proposed algorithm's results are compared to those of conventional and specialised WSAs. In comparison to previous methodologies, the average results demonstrate significant improvements of about 43% in execution time, 28% in energy consumption and 10% in total cost without any trade-offs.