Reinforcement Learning based FragmentAware Scheduling for High Utilization HPC Platforms Due to high capacity and complex scheduling activities, a HPC platform often creates resource fragments with low usability. This paper develops a novel fragmentaware scheduling approach which improves system utilization by fitting elastic lightweight tasks to the fragments of resources dynamically. The new approach employs a threshold to determine the balancing factor between the length of tasks and the degree of granularity of the resource fragments. We employ the PPO reinforcement learning approach to train a neural network that can compute the threshold precisely. With the threshold that is adaptive to the changing system states, the PPObased scheduler is able to utilize the idle resources and maximize the execution success rate of the tasks.

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.