Detection of Partial Task Graph Using Deep Learning Task scheduling is one of the optimization methods for parallel processing of programs. Task scheduling is intended to minimize execution time by allocating processing unit called task to appropriate computational resources. This method is considered to be impractical for large scale problems with the conventional search algorithms based on branch and bound method because of its computational complexity. One of the methods to solve this problem is to partially detect task graph and hierarchically conduct partial scheduling and complete scheduling. This can reduce computational complexity. But, this also causes another problem because the computation for detecting partial task graph itself is complicated. This research aims to solve this problem by using Deep Learning for detecting partial task graphs.

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.