## 1 Introduction  
In recent decades, cloud computing has been one of the growing paradigms which dynamically virtualizes resources to provide services over web pages [1; 2]. The primary objectives of cloud computing are to achieve high reliability, reputation, throughput, accessibility, scalability, and ease of use [3]. However, effective scheduling of tasks is a main concern which facilitates the execution of tasks utilizing the available resources in cloud computing environments [4]. In cloud computing, the task-scheduling problem has gained more attention among researchers due to the applications and the growth of cloud systems. Generally, the jobs scheduled by a user are assigned to capable devices in cloud systems where every job has consecutive tasks [5]. In cloud platforms, the resources are accessed in two ways: (i) using service-level agreements for resource allocation that act as an interface between the resources and applications, and (ii) brokers or users accessing the resources based on their input [6; 7].  
The objectives of task scheduling mainly include enhancing load balancing ability and resource utilization and reducing energy consumption and task completion time [8]. Improving the ability of load balancing prevents Virtual Machines (VMs) from resource overload, and the reduction of time for task completion improves the users' experience [9]. In addition to this, task scheduling focuses on many Quality of Service (QoS) factors like scalability, availability, throughput, and response time. The highly appropriate resources are utilized for executing tasks based on user requirements [10; 11]. In the present decades, several optimization algorithms have been implemented for task scheduling in cloud computing. The traditional optimization algorithms are ineffective in obtaining optimal task allocation due to a poor convergence rate and search ability. The ineffective task scheduling algorithms increase the execution time of the tasks and reduce the throughput of the cloud systems [12; 13]. Therefore, a novel SCEHO-IPSO algorithm is proposed in this manuscript for effective task scheduling. The contributions are outlined below:  
* Proposed a SCEHO-IPSO algorithm to resolve task-scheduling problems in the cloud computing platforms. In this scenario, the scheduler effectively ranks user tasks based on execution time and memory details.
* Based on the capacity criteria, the SCEHO-IPSO algorithm determines the efficient VMs to execute tasks in the queue. The SCEHO-IPSO algorithm simultaneously enhances resource utilization and decreases the makespan value.
* The SCEHO-IPSO algorithm optimizes task scheduling by identifying the optimal solutions with better convergence rates. The effectiveness of the SCEHO-IPSO algorithm is analyzed by conducting different experiments. The performance measures cost, execution time, makespan, and latency, and memory storage demonstrates the efficacy of the SCEHO-IPSO algorithm over other optimization algorithms.  
This manuscript is arranged in this manner. The research papers on the topic \"task scheduling\" are surveyed in Section 2. The methodology details, numerical results, and the conclusion of this manuscript are denoted in Sections 3-5, respectively.