## 1 Introduction  
The cloud computing environment allows consumers to access and utilize various resources without owning the infrastructure. The cloud environment consists of various features, such as service types, cloud protocols, and activities, as well as cloud deployment models that include public, private, community, and hybrid models. In addition, it contains three service models: software as a service (SaaS), infrastructure as a service (IaaS), and platform as a service (PaaS). Cloud consumers are given access to the resources in accordance with the service level agreement (SLA). Cloud technology enables consumers to gain several benefits, such as minimizing operating costs, improving flexibility, and increasing mobility. However, to ensure a high level of consumer satisfaction, both consumers and cloud service providers (CSPs) should consider the quality of service (QoS). Consumers pay for cloud services, whereas CSPs provide on-demand services. In general, the service contract (or SLA) specifies the cloud service's cost based on the quantity and quality of service. According to SLA standards, identifying an effective solution for handling consumers' requests is required even as the number of cloud consumers grows [1]. Service providers such as Amazon and Google have introduced a variety of cloud services in response to the rapid development of cloud computing, enabling users to manage enormous datasets operating on remote servers without having to install and execute on local devices [2]. Management of the resources required to fulfill consumer needs falls under the purview of the service provider. CSPs schedule consumer tasks and efficiently allocate computing resources by using a variety of scheduling techniques. Various kinds of pre-configured virtual machines (VMs) are used to provide these computing resources. The VM instances can be selected by consumers depending on their application needs and concerns related to cost. In a cloud environment, task-scheduling algorithms aim to support the scheduling of tasks to suitable cloud resources so that optimal performance can be attained. The execution of tasks in a particular order and how resources are allocated to tasks affect the performance of cloud systems. Through effective task scheduling and resource management, service providers seek to boost revenues and fully utilize resources. Scheduling techniques, such as particle swarm optimization (PSO) [3], the genetic algorithm (GA) [4], and game theory [5], are commonly used to schedule consumers' tasks in a cloud environment. These tasks are managed while considering various criteria, such as the load and size of the task, the execution time, and the availability of resources [6]. To effectively handle the resources, a resource management system that can vigorously schedule and allocate resources is required [7]. For a specific task that will boost performance and must be finished on time, cloud consumers search for the most resources possible. Cloud services provided by different service providers differ in terms of their performance. Due to the various data centers and technologies applied, services provided by the exact service provider can also vary in terms of performance. Globally, data center capacity in different forms is quickly rising. Inadequate load balancing against available resource allocation is the primary cause of rising server interruption rates. The most commonly used criteria for evaluating cloud service performance are the QoS attributes. To address the issue of measuring cloud-based services, the Cloud Services Measurement Initiative Consortium (CSMIC) has suggested a hierarchical Standard Measurement Index (SMI) [8]. Usability, accountability, assurance, agility, security and privacy, financial aspects, and performance are the seven categories that the framework uses to measure cloud-based services. In addition, three or more attributes are included in each category, resulting in a total of 51 sub-attributes.