# An Enhanced Discrete Symbiotic Organism Search Algorithm for Optimal Task Scheduling in the Cloud
## 2 Related Work
### Metaheuristic Scheduling Strategies in the Cloud  
Several works [7; 8; 9; 14; 15; 17; 22; 29; 31; 32; 38; 39; 40; 41; 42; 43; 44; 45] on metaheuristic scheduling strategies have been carried out to solve problems of task assignment with regard to minimising the makespan, degree of imbalance, convergence speed, and response time. Furthermore, the strategies have been shown to be good for finding an optimal task-resource assignment scheme that tries to minimise the computational cost, maintains a good quality of service, and increases the utilisation of computing resources. SOS, CS, CSO, PSO, ACO, GA, and their different versions are among the commonly utilised nature-inspired strategies and are based on populations found in the cloud. In most situations, SOS outperforms PSO, GAs, and ACO [9; 24; 37] and has a faster execution time. SOS is simple to implement as a parameter-free strategy in comparison with other population-based strategies. Furthermore, problems to do with workflow scheduling have been researched to a great extent by using PSO [29; 30; 46; 47], with the sole aim of minimising the cost of communication and the makespan. Likewise, independent task scheduling has additionally been carried out in the cloud by utilising SOS [7; 9; 14; 41] and PSO [43; 44; 48], and both were shown to reduce the makespan. Enhanced and hybrid variants of PSO [5; 26; 29; 47; 48] were also proposed for task scheduling in the cloud environment and were found to be a solution that was superior to GAs and ACO. Consequently, the SOS algorithm has recently gained popularity in terms of its robustness and the efficiency of the metaheuristic algorithm. As such, the SOS algorithm has attracted researchers' attention for its application in solving optimisation problems that arise from numerous disciplines, such as science and engineering. Moreover, Table 1 shows a summary of the applications of the SOS algorithm. Many attempts have been successful in adapting and modifying SOS algorithms to address multi-objective and constrained optimisation, which are important aspects in facilitating the design and optimisation of several problems in engineering, as well as in computer science. While considerable success has recently been found in these domains, the optimisation of problems in these domains remains an active research issue. As such, SOS algorithms have been applied in order to solve optimisation problems in different areas, such as machine learning, construction project scheduling, power optimisation, design optimisation of engineering structures, energy optimisation, transportation, wireless communication, and economic dispatching. Based on this trend of the use of the SOS for optimisation problems, it has been proven to provide all-purpose principles that can be easily adapted in order to solve an extensive range of optimisation problems in many areas, which prompted the authors of this study to utilise SOS to solve a large-scale task scheduling problem in a cloud computing setting. Consequently, this paper presents SOS-based variant strategies for scheduling large-scale tasks in an IaaS cloud computing setting. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Area** & **Application** & **Reference** \\ \hline \multirow{4}{*}{Machine learning} & This method maps the SOS algorithm from a continuous space to a discrete space using an adaptive \(\mathbb{S}\)-shaped transfer function, and it can be used to search for the optimal feature subset in a feature selection space. & [49] \\  & Input parameter optimisation in data classification. & [50] \\ \hline \multirow{4}{*}{Construction project scheduling} & This work developed a new ensemble model called the evolutionary neural machine inference model (ENMIM) in order to estimate energy consumption in residential buildings based on actual data. An optimisation tool is necessary for a construction management system in order to develop the desired construction schedule and to save time and costs. & [52] \\  & Time, cost, and labour utilisation trade-off.