Cloud computing provides the resources for computing tasks on demand over the Internet in the remote Cloud. This computing has merits such as high computing power, low services cost, better performance, scalability, accessibility as well as availability. The data centers are massively scalable and can be ubiquitously accessed from any device, anywhere, at any time worldwide. The efficient usage of cloud computing resources is based on resource scheduling and task allocation. Task scheduling is an essential and most important part of a cloud computing environment since it mainly focuses on enhancing the efficient utilization of resources and reducing task completion time. It also considers other parameters in scheduling algorithms, such as task completion cost. The main aim of the task scheduling algorithm is to improve the performance and quality of service and maintain efficiency among the tasks and reduce the cost. Proper scheduling of the task plays a crucial role in utilizing the resources. There are several types of task scheduling methods that works based on the priority of the task. They are:
Preemptive scheduling
  In this type, the scheduling process takes place based on the priority of the task that is the currently running task gets interrupted when a new task arrives since the running task has a priority less than the new task. At this time, the low priority suspends its execution, and the high priority process starts its execution. It maximizes CPU utilization and throughput and minimizes turnaround time, waiting time, and response time.
Non-Preemptive scheduling
  In this type, the scheduling process considers the arrival of the task and not the time that is the execution of the task takes place in the first-come, first-serve manner. The task completes its execution without waiting for the newly arriving task.
  There are few more scheduling based on the usage of the preemptive and non-preemptive scheduling
First Come First Serve (FCFS)
  In the First Come First Serve process, the oldest task in the queue is executed first. As a result, the waiting time is long. It schedules purely based on non-preemptive scheduling.
Shortest job first (SJF)
  In the First Come First Serve process, the oldest task in the queue is executed first. As a result, the waiting time is long. It schedules purely based on non-preemptive scheduling.
Priority scheduling
  In priority scheduling, the priority is assigned to all the tasks, and this priority is based on the CPU, memory usage, or the choice of users. Here, the scheduling is based on the priority of the task. It also schedules using either preemptive or non-preemptive scheduling.
Round Robin (RR) scheduling
  •  The Round Robin scheduling is similar to the FCFS scheduling with the time-slicing and preemption technique. In this scheduling, the first task begins its execution based on FCFS and completes its process within its allocated time. Similarly, the next task is executed within the time, and the previous task waits for its turn to continue its execution.
  •  There is a specific task scheduling strategy for the effective utilization of the resources, which are described as follows: Several existing systems propose the task scheduling using the container, priority, and heuristic algorithm such as greedy algorithm, Bandwidth-Aware divisible Task Scheduling (BATS), Modified Analytic Hierarchy Process (MAHP), Longest Expected Processing Time preemption (LEPT) and divide and conquer algorithm. Here one of the existing algorithms uses the container-based task scheduling algorithms by using the containers in task processing. This approach either accepts or rejects the incoming task, and the accepted tasks are completed within the delay constraint.
  •  However, both the precedence of the task and load balancing should be considered in the workflow task.
  •  In the dynamic scheduling, based on the user-s budget, the resources are located and released several times. It increases time consumption. Hence, it results in the violation of the SLA and minimizes the service provider-s performance. Placing the resources in the distributed data center involves exchanging the information; thus, Most of the researcher models focus their task scheduling in the single data center. However, it is necessary to consider the data transfer while performing resource scheduling. Thus, it is essential to define specific parameters before performing the task scheduling in multiple data centers. During task scheduling in the various data centers, both the time and the cost are essential to include. Though most researchers focus on the energy, time, and cost, they fail to contribute to the remaining metrics, for instance, storing, processing, and scheduling over multiple resources. Furthermore, few researchers perform the migration for balancing the overloaded resources; however, execution of migration increases the cost, power consumption, and makespan. Therefore, it is necessary to implement effective resource allocation and task scheduling that helps to maintain the resources in a balanced state.