dc.contributor.author |
Radhika, S |
|
dc.contributor.author |
Keshari Swain, Sangram |
|
dc.contributor.author |
Adinarayana, S |
|
dc.contributor.author |
Ramesh Babu, BSSV |
|
dc.date.accessioned |
2024-04-24T15:32:52Z |
|
dc.date.available |
2024-04-24T15:32:52Z |
|
dc.date.issued |
2024-04-24 |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5604 |
|
dc.description.abstract |
Cloud computing has transformed data management with its scale and flexibility. Cloud resources are transient and diversified,
making task scheduling difficult. This paper proposes Double Deep Q-Network (DDQN) reinforcement learning model to solve the cloud
computing task scheduling problem. Double Deep Q-Network (DDQN) is a powerful reinforcement learning system that improves on
Deep Q-Networks (DQN). The target network and the online network are the two distinct neural networks that DDQN presents. To create
a more consistent and less unpredictable learning process, the target network is updated on a regular basis to imitate the Q-value estimations
of the online network. Traditional DQN can have problems with overestimation bias, which is something that this dual-network architecture
helps to alleviate. DDQN is a reliable and efficient tool for solving complex reinforcement learning problems. It excels in learning optimal
strategies through iteratively improving its Q-value estimations. DDQN presents a robust framework for addressing the challenges inherent
in cloud computing task scheduling. Its dual-network architecture and iterative learning process offer a promising avenue for enhancing
the efficiency and effectiveness of resource allocation in cloud environments. Through its continuous refinement of Q-value estimations,
DDQN emerges as a valuable asset in navigating the complexities of modern data management within cloud infrastructures. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Cloud computing, Data management, Task scheduling, Double Deep Q-Network (DDQN), Reinforcement learning, Deep QNetworks (DQN), Target network, Online network. |
en_US |
dc.title |
Efficient Task Scheduling in Cloud using Double Deep QNetwork |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/XXXXXX |
|
dc.identifier.doi |
2210-142X |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
11 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
Department of CSE, Centurion University of Technology and Management & Department of CSE, Raghu Engineering college |
en_US |
dc.contributor.authoraffiliation |
Department of CSE, Centurion University of Technology and Management |
en_US |
dc.contributor.authoraffiliation |
Department of CSSE, Andhra University college of Engineering |
en_US |
dc.contributor.authoraffiliation |
Department of ECE, Raghu Engineering college |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |