research-article

Resource scheduling approach in cloud Testing as a Service using deep reinforcement learning algorithms

Authors:

Priyadarsini Karthik,

Karthik SekharAuthors Info & Claims

CAAI Transactions on Intelligence Technology, Volume 6, Issue 2

Pages 147 - 154

https://doi.org/10.1049/cit2.12041

Published: 05 April 2021 Publication History

Abstract

Many organizations around the world use cloud computing Testing as Service (Taas) for their services. Cloud computing is principally based on the idea of on‐demand delivery of computation, storage, applications, and additional resources. It depends on delivering user services through Internet connectivity. In addition, it uses a pay‐as‐you‐go business design to deliver user services. It offers some essential characteristics including on‐demand service, resource pooling, rapid elasticity, virtualization, and measured services. There are various types of virtualization, such as full virtualization, para‐virtualization, emulation, OS virtualization, and application virtualization. Resource scheduling in Taas is among the most challenging jobs in resource allocation to mandatory tasks/jobs based on the required quality of applications and projects. Because of the cloud environment, uncertainty, and perhaps heterogeneity, resource allocation cannot be addressed with prevailing policies. This situation remains a significant concern for the majority of cloud providers, as they face challenges in selecting the correct resource scheduling algorithm for a particular workload. The authors use the emergent artificial intelligence algorithms deep RM2, deep reinforcement learning, and deep reinforcement learning for Taas cloud scheduling to resolve the issue of resource scheduling in cloud Taas.

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Cited By

Clement JSriharipriya KPrakasam PS C(2024)Throughput enhancement in a cognitive radio network using a reinforcement learning methodMultimedia Tools and Applications10.1007/s11042-023-15432-883:1(1165-1187)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15432-8
Li J(2023)Optimal Scheduling of Real-Time Learning Resources Using Intelligent Algorithms in Online Learning EnvironmentsProceedings of the 2023 International Conference on Information Education and Artificial Intelligence10.1145/3660043.3660184(792-798)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3660043.3660184

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cover image CAAI Transactions on Intelligence Technology

CAAI Transactions on Intelligence Technology Volume 6, Issue 2

June 2021

118 pages

EISSN:2468-2322

DOI:10.1049/cit2.v6.2

Issue’s Table of Contents

© 2021 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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John Wiley & Sons, Inc.

United States

Publication History

Published: 05 April 2021

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Cited By

Clement JSriharipriya KPrakasam PS C(2024)Throughput enhancement in a cognitive radio network using a reinforcement learning methodMultimedia Tools and Applications10.1007/s11042-023-15432-883:1(1165-1187)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11042-023-15432-8
Li J(2023)Optimal Scheduling of Real-Time Learning Resources Using Intelligent Algorithms in Online Learning EnvironmentsProceedings of the 2023 International Conference on Information Education and Artificial Intelligence10.1145/3660043.3660184(792-798)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3660043.3660184

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