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Cloud is a challenging and highly demanding system where services are metered, reliable, and can be accessed on-demand Yang and Chen (2010), Zhang et al. (2010), Sandhu and Lakhwani (2022), Sorkhoh et al (2020). Workflows Zhao et al. (2011) have been used to model scientific applications Barker and Van Hemert (2007), Pietri et al. (2013), Gil et al. (2007). Scientific workflows like MONTAGE, LIGO, SIPHT, GENOME, etc. have millions of tasks. Zhao et al. (2011), Vöckler et al. (2011), Kouatli, I (2020). These tasks must be mapped to cloud resources as they become feasible to provide efficient scheduling with the least amount of resource consumption. There are varieties of optimization approaches that may be used to find optimal scheduling solutions (Casavant and Kuhl, 1988). To address different optimization problems numerous general-purpose meta-heuristic algorithms are available (Talbi, 2009). These algorithms provide scheduling and optimization solutions that are close to optimum (Kumar and Sivakumar, 2022; Bisht and Vampugani, 2022; Alakbarov, 2022), Farhat et al (2020) .
Meta-heuristic approaches are generally more computationally intensive than heuristic approaches and take longer to run; however, they also tend to find more desirable schedules as they explore different solutions using a guided search. In cloud systems, using meta-heuristics approach to solve the workflow scheduling problem involves many challenges such as: modeling a theoretically unbound number of resources, defining operations to avoid exploring invalid solutions (e.g., data dependency violations) to facilitate convergence, and pruning the search space by using heuristics based on the cloud resource model (Negi et al., 2013; Rajput et al., 2022; Kumar et al., 2022).
In the recent era, single-objective and multi-objective-based tasks scheduling (Vöckler et al., 2011, Vecchiola et al., 2009, Malawski et al., 2015, Hammoud et al. 2020) and mapping algorithms have been used by researchers in cloud environment (Holland, 1992, Rodriguez and Buyya, 2017, Mehta et al., 2009, Deelman et al., 2015, Verma and Kaushal, 2015). There are many promising studies that provide efficient scheduling of input tasks in cloud systems (Yu and Buyya, 2005, Calheiros et al., 2011, Arisdakessian,et al. 2020, Mishra et al. 2021). Still, research demands improvements in existing meta heuristic algorithms so that resources can be utilized at maximum.
Tasks scheduling in cloud computing is a fast and demanding area of research. In cloud computing, plenty of tasks runs concurrently and use the resources online. The scheduling reduces the computation time and processing time of tasks (Alkhanak et al., 2016, Liu et al., 2017, Reddy and Kumar, 2017, Rimal and Maier, 2016, Zhu et al., 2015, Wahab, O. A., et al. 2017). Different types of algorithms and techniques (Nasr et al., 2014, Singh and Singh, 2013, Zhang et al., 2017, Priya and Kiranbir, 2018, Huang et al., 2013, Abrishami et al., 2013, Arabnejad et al., 2016, Ghose et al., 2017, Shaw and Singh, 2014, Shi and Dongarra, 2006, Delavar and Aryan, 2014, Alkhanak and Lee, 2018, Al-Qerem et al. 2020, Kuppusamy, P., et al. 2022) used for task scheduling in the cloud system are categorized in Figure 1.
Figure 1. Dependent task scheduling