# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 1 Introduction  
With the rapid development of the Internet of Things (IoT) we have entered a new era of digitalization connecting various smart devices and sensors and enabling interconnectivity and information sharing among them. However, the rapid growth of the IoT brings with it a series of challenges and issues, one of which is effectively handling the massive influx of task requests generated by devices. In the IoT, a vast number of devices and sensors generate an enormous amount of data processing tasks that need to be collected, stored, analyzed, and processed. Traditional methods of task processing often struggle to cope with such a tremendous task scale and high task generation rate. Cloud computing technology provides robust support for addressing this problem. Based on a network, cloud computingoffers efficient computing and storage resources, making the processing of large-scale tasks feasible. By connecting IoT devices to cloud platforms, tasks generated by the devices can be uploaded to the cloud in real-time for processing. Cloud infrastructure provides the necessary computational power, storage capacity, and scalability to efficiently handle the influx of tasks. This enables real-time and scalable processing of tasks generated by IoT devices, facilitating data analysis and decision-making while providing valuable insights. Cloud computing empowers the IoT ecosystem by offloading the computational burden from individual devices to the cloud, ensuring efficient task management and enabling advanced data processing and analytics. The integration of IoT and cloud computing facilitates the seamless and effective handling of the vast volume of tasks generated by IoT devices, paving the way for enhanced IoT applications and services in the digital age. A vast amount of resources such as computing power, storage capacity, and network bandwidth are available in the cloud computing environment. However, these resources need to be allocated reasonably among multiple users and tasks in order to meet the needs and priorities of different users. Resource allocation in the dynamic and heterogeneous cloud computing environment can become highly complex and challenging. First, tasks and user demands in the cloud computing environment exhibit great diversity and variability; different tasks may require different types and scales of resources, and user demands for resources can change over time. Second, resources in the cloud computing environment are limited. Due to the scarcity of resources, it is necessary to allocate resources effectively in order to maximize user satisfaction and provide high-performance services. Load balancing of resources needs to be considered to avoid performance degradation caused by resource overload; moreover, resource conflicts and competition inevitably exist in the cloud computing environment. Multiple tasks or users may compete for the same resources simultaneously, leading to resource contention and increased delays. Therefore, an effective resource allocation scheduling strategy is needed in order to address resource conflicts and provide fair and efficient resource utilization. To address the performance issues in task scheduling [1; 2; 3], researchers have proposed various novel research methods. Existing cloud computing task scheduling methods typically fall into the following categories. (1) Static Scheduling Methods: these methods determine the execution order and allocation scheme of tasks before task submission, such as Shortest Job First (SJF), Earliest Deadline First (EDF), and Minimum Remaining Time (MRT). While these methods are simple to implement, they cannot adapt to dynamic environments and changing task demands. (2) Heuristic Scheduling Methods: these methods make task scheduling decisions based on past experience and on rules such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). These methods consider certain task priorities and resource utilization efficiency; however, they lack global optimization and dynamic adjustment capabilities.