# A Novel Fault-Tolerant Aware Task Scheduler Using Deep Reinforcement Learning in Cloud Computing
## 2 Related Works  
In [7], the authors proposed a self-learning methodology for energy-conscious work scheduling in a heterogeneous cloud computing environment. It presents a reinforcement learning-based energy-conscious task scheduling method which obtains results in terms of throughput and energy consumption. The authors of [8] proposed a novel task scheduling technique called QL-HEFT. It reduces makespan by using Q-learning HEFT. QL-HEFT is divided into two phases, i.e., task sorting using Q-learning to acquire order and the processor allocation phase. Both phases are essential to the overall functioning of the algorithm. Experiments demonstrate that QL-HEFT delivers better results in terms of average response time and makespan. In [9], a deep reinforcement learning combined with long short-term memory (DRL-LSTM) approach is developed with which experiments are conducted with real-world datasets retrieved from the Google Cloud Platform, which demonstrated the DRL-LSTM strategy. Compared with existing techniques, it is more effective in terms of considered parameters. DRL-LSTM reduces the cost of using the computer's processing power in comparison to SJF, RR, and IPSO. The authors of [10] designed a scheduler using a Q-learning-based agent which operates at VM level to schedule tasks using a pricing model of VM instances. The authors of [11] formulate the online scheduling problem as EMDP to construct a deep risk-sensitive reinforcement learning algorithm that accounts for the task arrival dynamics. It evaluates risk and energy consumption as a constraint. For each phase, it calculates optimal parameters which minimizes delay and risk. The simulation results indicate that the proposed algorithm reduced processing time of tasks. The authors of [12] present an artificial bee colony with a Q-learning algorithm, which is a reinforcement learning technique. The MOABCQ method is an independent task scheduling approach for cloud computing. It is proposed to tackle throughput, load balancing, and resource utilization. Cloudsim was used to compare the efficiency of the proposed method with existing load balancing and scheduling techniques. Experimental results indicated that MOABCQ tackles cost, throughput, makespan, degree of imbalance, and average resource utilization. The authors of [13] provide an effective approach for job scheduling in the specified cloud that makes use of the MapReduce framework [14], GA-WOA. Task features are first retrieved from the client's task. Enormous tasks are then divided into various tasks using the MapReduce framework. Finally, the GA-WOA algorithm is used to effectively allocate tasks. The authors of [15] developed a new deep-reinforcement-learning-based RP, TS system designed to cut down on energy costs on a large-scale basis for CSPs with a lot of servers which receive huge requests from users. A two-stage RP-TS processor based on deep Q-learning is developed to automatically make the best decisions for the future to adapt to the new environment by learning, e.g., user request patterns, the real price of electricity. The authors of [16] builds a scheduler by improving the DQN method and introducing a noise network to create the novel NoisyDQN algorithm, which both expands the scope of the reinforcement learning model's exploratory capabilities and raises the ceiling of its possible performance. Experimental results show that the proposed algorithm significantly decreases the average standard deviation of cluster CPU utilization, which is achieved by decreasing the cluster's power consumption, decreasing user waiting time, and increasing response speed and load balancing. In [17], the authors focused on minimization of makespan, cost, and average delay when working under restrictions of VMs and sensitive deadlines. They developed a constraint-adaptive online task scheduling method using double deep Q-learning. They used Gaussian distribution of relevant parameters as part of the state space, which mitigates the effect of increasing the number of VMs on the input dimension of model.