# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 1 Introduction  
With the rapid development of computer technology and the internet economy, cloud computing, as the cornerstone of big data and artificial intelligence (AI), is one of the most promising and valuable research directions, and efficient task scheduling has always been the goal and challenge of research in this field [1]. Considering that the services and resources provided by cloud service providers have increased significantly in the past ten years, and their workloads exhibit a high degree of dynamic change, how to adapt to the online task scheduling environment with dynamic changes has become an important issue [2]. AI is an emerging research topic, and it can be used to solve complex problems and find optimized solutions in many applications and fields. Hence, it can potentially solve the task scheduling problem in software as a service (SaaS). The benefits of AI in science, medicine, technology, and the social sciences have been proven. With the popularity and development of cloud computing, the process of task scheduling will generate a large number of records, and these data can be used to establish a data-driven AI task scheduling system. Training through the data continuously generated during scheduling helps in the formation of strategies according to the current environment. Although AI technology has made significant progress, its use in systems that require reliability, transparency, and maintainability is still in its infancy. Task scheduling and resource allocation in the cloud environment usually have the optimization goals of reducing makespan, improving resource utilization, load balancing, cost optimization, and reducing energy consumption [3]. Previous studies have proposed a solution for scheduling optimization for one goal or even multiple goals [4]. Practicalresearch must consider multiple cloud computing task scheduling objectives. Current methods can be categorized as heuristic algorithms, meta-heuristic algorithms, and reinforcement learning (RL) [5]. Most schemes consider the scheduling of offline batch tasks, and cannot be applied to dynamically changing workloads in online task scheduling [2]. We model the online task scheduling problem of cloud computing and propose server-level agreement constraint adaptive online task scheduling algorithm based on double deep Q-learning (SLA-DQTS), an algorithm based on deep reinforcement learning (DRL) [6] under server level agreement (SLA) constraints. We consider the dynamic change of the cloud computing environment and task load through the design of the state space and reward function to adaptively learn the scheduling strategy between the task load and number of VMs, and we use reinforcement learning to maximize the cumulative reward to acquire a long-term decision-making strategy. The main contributions of this work are summarized as follows:  
* With the widespread application of DRL in task scheduling, we propose an intelligent task scheduling framework using DDQN in cloud computing online task scheduling to optimize the allocation decision of online tasks to virtual machines (VM). In the makespan and cost optimization problems under the constraints of the SLA, the corresponding scheduling strategy is learned according to the load situation. * Considering the dynamics of the cloud environment, we design a state-action space and reward function. As the environment load changes, the reward function switches the main optimization goal. Using the Gaussian distribution of related features as the state space, the input dimension of the model remains unchanged under different numbers of VMs. The reward function allows the model to adapt to changes in the task load. The fixed-dimensional state space makes it unnecessary to change the model with the number of VMs. The rest of this article is organized as follows. Related research is discussed in Section 2. Section 3 introduces the key stages of online task scheduling and proposes a problem model.