Task Scheduling in Cloud Using Deep Reinforcement Learning

Cloud computing is an emerging technology used in many applications such as data analysis, storage, and Internet of Things (IoT). Due to the increasing number of users in the cloud and the IoT devices that are being integrated with the cloud, the amount of data generated by these users and these devices is increasing ceaselessly. Managing this data over the cloud is no longer an easy task. It is almost impossible to move all data to the cloud datacenters, and this will lead to excessive bandwidth usage, latency, cost, and energy consumption. This makes it evident that allocating resources to users' tasks is an essential quality feature in cloud computing. This is because it provides the customers or the users with high Quality of Service (Qo S) with the best response time, and it also respects the established Service Level Agreement. Therefore, there is a great importance of efficient utilization of computing resources for which an optimal strategy for task scheduling is required. This paper focuses on the problem of task scheduling of cloud-based applications and aims to minimize the computational cost under resource and deadline constraints. Towards this end, we propose a clipped double deep Q-learning algorithm utilizing the target network and experience relay techniques, as we as using the reinforcement learning approach. (C) 2021 The Authors. Published by Elsevier B.V.

Jaar van publicatie:2021

Trefwoorden:task scheduling, computational cost, energy consumption, deep reinforcement learning, Clipped Double Deep Q-learning (CDDQL)

BOF-keylabel:ja

IOF-keylabel:ja

Toegankelijkheid:Open