# A Discrete Prey-Predator Algorithm for Cloud Task Scheduling
## 2 Related Work  
The cloud computing environment utilizes numerous task-scheduling algorithms, including heuristic, metaheuristic, and hybrid algorithms, to achieve efficient task execution times with optimized throughput systems. In some studies, heuristic algorithms such as the max-min algorithm and the SJF (Shortest Job First) algorithm have been employed for cloud job scheduling. [PERSON] et al. utilized the max-min algorithm to develop a task allocation algorithm that prioritizes speed and minimizes response time and makespan [22; 23]. Furthermore, [PERSON] et al. optimized the max-min algorithm to compute the expected execution time [24]. [PERSON] et al. proposed an algorithm based on the SJF algorithm to reduce execution and turnaround time by selecting the task with the shortest execution time [25]. [PERSON] et al. presented the Shortest Job First (SJF) algorithm to create a scheduling mechanism that reduces completion time while maximizing resource utilization [22]. In metaheuristic mechanisms, authors [PERSON] et al. introduced a task-scheduling mechanism based on particle swarm optimization (PSO) to allocate tasks to available resources to minimize execution time [26]. The implementation of particle swarm optimization (PSO) by [PERSON] et al. focuses on task scheduling by dividing tasks into subtasks and allocating each subtask to a suitable resource using a random order. This approach decreases the fitness value, makespan, and total completion time. [PERSON] et al. presented an algorithm for task scheduling based on the ant colony optimization (ACO) mechanism [27]. The goal of the ACO mechanism is to minimize the makespan [28; 29; 30]. Several algorithms utilized the genetic algorithm for optimizing the cloud-task-scheduling process [31]. The researchers in [7] applied the firefly algorithm as an optimization method for scheduling jobs in cloud computing [32]. The firefly mechanism was introduced to minimize the execution times. The study in [33] proposed an Ions Motion Optimization (IMO) algorithm for scheduling cloud jobs. The IMO mechanism consists of two phases, the liquid and the crystal phases. These phases maintain a balance between rapid convergence and avoiding local optima in the algorithm's performance [33]. The authors in [17] converted uncertain factors into interval parameters, effectively and concurrently encompassing factors like makespan, scheduling cost, and task completion rate into the scheduling process. Subsequently, they introduced a new model, the Interval Many-Objective Cloud job Scheduling Optimization (I-MCTSO) model, tailored to replicate real-world cloud computing job-scheduling scenarios. Evolution optimization has been implemented in several works for cloud task scheduling [34; 35; 36]. The study [34] introduced a new job-scheduling methodology for heterogeneous cloud environments, utilizing the innovative Advanced Phasmatodea Population Evolution algorithm. This algorithm expedites the solution discovery process by enhancing the convergence of the closest optimal solutions. Additionally, the study incorporates a restart strategy to safeguard against local optimization, maintaining a harmonious equilibrium between exploration and development capabilities within the algorithm. The authors in [35] introduced a dual-phase metaheuristic job-scheduling mechanism for cloud computing. Initially, the mechanism employs a clustering method to organize computing nodes into efficient clusters. Each node undergoes training across various utilization levels. Subsequently, they combined the Sparrow Search metaheuristic with the differential evolution (DE) mechanism to enhance the search efficiency for identifying suitable job-resource combinations. A new job scheduler to address the task-scheduling challenges within the cloud computing environment is proposed in [37]. The proposed job-scheduling method is constructed upon two novel enhancements in the conventional differential evolution approach.