# SLA-DQTS: SLA Constrained Adaptive Online Task Scheduling Based on DDQN in Cloud Computing
## 2 Related Work
The experimental results show that the proposed task scheduling scheme can optimize cloud resource utilization and load balancing to obtain the minimum response time under resource constraints. [PERSON] et al. [25] aimed at hybrid cloud, which is a joint service of multiple clouds. They modeled the problem of each scheduler as MDP, proposed a hybrid cloud distributed scheduling algorithm, and used Q-learning to obtain a suitable scheduling strategy. The result proves that compared with the five typical dynamic scheduling algorithms (opportunistic load balancing, shortest execution time, shortest completion time, handover algorithm, and k-percent best), the scheme has a short average response time and high collaboration efficiency. [PERSON] et al. [26] proposed a dynamic, fault-aware framework that can be integrated into a Hadoop scheduler. The framework relies on ML algorithms and a Markov decision process (MDP) to generate scheduling strategies. The framework was deployed with ATLAS+, an adaptive fault-aware scheduler for Hadoop. The performance of ATLAS+ was compared with that of the FIFO, Fair, and Capacity Hadoop schedulers, and it was found to be better in terms of the number of failed jobs, total execution time, and resource utilization. [PERSON] et al. [27] describes TeNOR, a micro-service-based network function virtualization orchestrator capable of effectively addressing resource and network service mapping. One of the resource mapping strategies uses reinforcement learning. Through iterative learning of the best strategy for resource mapping, the acceptance rate of the RL method in large-scale NS has increased significantly in experiments. People have gradually turned their attention to DRL, with its potential for long-term learning and decision making, and it has been applied to some scheduling problems in cloud computing. [PERSON] [28] proposed GA-ANN, a neural-network-based scheduling algorithm providing better QoS. [PERSON] et al. [29] proposed the DQ-HEFT algorithm, combining DRL and the heterogeneous earliest completion time algorithm, using DQL for task sequencing and heterogeneous earliest finish time (HEFT) for task allocation. [PERSON] et al. [30] proposed a job scheduling scheme based on RL to optimize VM resources and deadlines while minimizing the construction time and average waiting time (AWT). [PERSON] et al. [31]proposed an AI algorithm, deep Q learning task scheduling (DQTS), to solve the task scheduling problem of a directed acyclic graph (DAG). [PERSON] et al. (2019) proposed a deep job scheduling (DeepJS) algorithm based on deep reinforcement learning to minimize makespan, and proved its convergence and generalization. [PERSON] et al. (2019) proposed a deep deterministic gradient strategy (DDPG) to find the optimal allocation plan that meets the requirements of the SLA and minimizes the average response time. It performed well at reducing average response time and balancing loads. [PERSON] et al. (2019) proposed a task scheduling algorithm based on a deep reinforcement learning architecture (RLTS) to minimize the task execution time. RL technology has been widely used in cloud computing, but the huge state space and action space of the cloud computing system make the traditional RL technology inapplicable ([PERSON] et al., 2019). Therefore, DRL technology has also begun to emerge in this area, and has shown its ability to solve high-dimensional problems.