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TopSeveral works have been proposed to solve the scheduling problem in cloud computing.
Authors in (Marphatia et al., 2013) presents GHPSO algorithm to achieve the scheduling goals, this paper greatly improves the solution quality, so it can be used as an effective way to solve the cost minimization problem in cloud computing.
Samal and Mishra., (2013) propose scheduling based on particle swarm optimization algorithm in cloud computing
The FCFS algorithm is considered as an easy method in scheduling algorithms, where processes are ordered by arrival time and submitted to the virtual machine (Samal and Mishra., 2013).
Awad et al., (2015), propose a scheduling algorithm integrated with task grouping, priority-aware and SJF (shortest-job-first) to reduce the waiting time and makespan, as well as to maximize resource utilization.
Navimipour and Milani., (2015) propose the Max-min algorithm, the Max-min improvement is based on execution time instead of completion time as a basis for selection.
Agarwal and Jain., (2014) propose the Round Robin (RR) algorithm focused on equity. RR uses the ring as a queue to store jobs. Each task in a queue has the same execution time and it will be executed in turn. If a job cannot be completed during its turn, it will be stored in the queue while waiting for the next turn. In addition to this, you need to know more about it.
A workflow tasks scheduling algorithm based on genetic algorithm in cloud computing is proposed in (Cui and Xiaoqing., 2018), In this algorithm, each task is assigned priority by a top-down levelling method for reducing the execution cost of workflow tasks scheduling, all workflow tasks are divided into the different levels, which can promote the parallel execution of workflow tasks.