A framework for scheduling in cloud manufacturing with deep reinforcement learning Cloud manufacturing is a novel serviceoriented networked manufacturing paradigm that aims to provide ondemand manufacturing cloud services to consumers. Scheduling is a critical means for achieving that aim. Currently, research on scheduling in cloud manufacturing is still in its infancy, and current frequently adopted metaheuristic algorithmbased approaches have some shortcomings, e.g. they require complex design processes and lack adaptability to dynamic environments. Deep reinforcement learning (DRL) that combines advantages of reinforcement learning and deep learning provides an efficient, adaptive and intelligent approach for solving scheduling problems in cloud manufacturing. However, to the best of our knowledge, there has been no application of DRL to scheduling in cloud manufacturing. This work conducts a preliminary exploration over this issue. First, a DRLbased framework for scheduling in cloud manufacturing is proposed. Then a DRL model for online singletask scheduling in cloud manufacturing is presented to demonstrate the effectiveness of the framework. DRL as a promising technique will find wide applications in cloud manufacturing, and this work can provide some reference for future research on this.

Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud With the widespread adoption of Internet of Thing (IoT) and the exponential growth in the volumes of generated data, cloud providers tend to receive massive waves of demands on their storage and computing resources. To help providers deal with such demands without sacrificing performance, the concept of cloud automation had recently arisen to improve the performance and reduce the manual efforts related to the management of cloud computing workloads. In this context, we propose in this paper, Deep learning Smart Scheduling (DSS), an automated big data task scheduling approach in cloud computing environments. DSS combines Deep Reinforcement Learning (DRL) and Long ShortTerm Memory (LSTM) to automatically predict the Virtual Machines (VMs) to which each incoming big data task should be scheduled to so as to improve the performance of big data analytics and reduce their resource execution cost. Experiments conducted using realworld datasets from Google Cloud Platform show that our solution minimizes the CPU usage cost by 28.8% compared to the Shortest Job First (SJF), and by 14% compared to both the Round Robin (RR) and improved Particle Swarm Optimization (PSO) approaches. Moreover, our solution decreases the RAM memory usage cost by 31.25% compared to the SJF, by 25% compared to the RR, and by 18.78% compared to the improved PSO.