Deep reinforcement learning based resource allocation strategy in cloud-edge computing system
A lot of real time processing as well as resourceintensive apps is what is needed more and thus, cloud-edge computing systems require compelling resource allocation schemes. This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is...
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| Format: | Conference or Workshop Item |
| Language: | en |
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2024
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| Online Access: | http://eprints.uthm.edu.my/11795/1/P17003_0e3c560e211e3d06995d79b44427688c.pdf%206.pdf http://eprints.uthm.edu.my/11795/ http://10.1109/ICICACS60521.2024.10498384 |
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| author | Ahmed Adhoni, Zameer Habelalmateen, Mohammed DR Janardhana, DR Janardhana Abdul Sattar, Khalid Nazim Audah, Lukman |
| author_facet | Ahmed Adhoni, Zameer Habelalmateen, Mohammed DR Janardhana, DR Janardhana Abdul Sattar, Khalid Nazim Audah, Lukman |
| author_sort | Ahmed Adhoni, Zameer |
| building | UTHM Library |
| collection | Institutional Repository |
| content_provider | Universiti Tun Hussein Onn Malaysia |
| content_source | UTHM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | A lot of real time processing as well as resourceintensive apps is what is needed more and thus, cloud-edge
computing systems require compelling resource allocation
schemes. This research focuses on the utility of Multiagent
Learning framework with Deep Reinforcement Learning
(MAL-DRL) which is used for solution deployment concerning
resource allocation in such systems, such that the end user
enjoys optimization while operators optimize resource
utilization. In this work, the research focus on the simulation
testing of the MAL-DRL algorithm against classical Random
Allocation (RA) and singe agent DRL methods. This exhibition
shows that MAL-DRL improves the average latency more (44%
savings) and at the same time more resources are used (35%
increase) compared both the alternatives with a combined
reward measure score of 0.80. These results show that
distributed decision-making and learning styles of MAL-DRL
brings together faster resource allocation which can be
interpreted as a reduction of users’ delay experience and
therefore lead to better performance of whole system. Although
the article limits simulations in areas to be covered and
complexity in training, it brings the prospective benefits of
MAL-DRL in the management of cloud-edge resources on the
spot. In the course of this undertake, the effect of ensuring
communication goals, moving learning strategies, and security
measures for the ultimate goal of boosting this method's
applicability in real-world antics can be explored |
| format | Conference or Workshop Item |
| id | my.uthm.eprints-11795 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | en |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my.uthm.eprints-117952025-03-26T02:56:21Z http://eprints.uthm.edu.my/11795/ Deep reinforcement learning based resource allocation strategy in cloud-edge computing system Ahmed Adhoni, Zameer Habelalmateen, Mohammed DR Janardhana, DR Janardhana Abdul Sattar, Khalid Nazim Audah, Lukman T Technology (General) A lot of real time processing as well as resourceintensive apps is what is needed more and thus, cloud-edge computing systems require compelling resource allocation schemes. This research focuses on the utility of Multiagent Learning framework with Deep Reinforcement Learning (MAL-DRL) which is used for solution deployment concerning resource allocation in such systems, such that the end user enjoys optimization while operators optimize resource utilization. In this work, the research focus on the simulation testing of the MAL-DRL algorithm against classical Random Allocation (RA) and singe agent DRL methods. This exhibition shows that MAL-DRL improves the average latency more (44% savings) and at the same time more resources are used (35% increase) compared both the alternatives with a combined reward measure score of 0.80. These results show that distributed decision-making and learning styles of MAL-DRL brings together faster resource allocation which can be interpreted as a reduction of users’ delay experience and therefore lead to better performance of whole system. Although the article limits simulations in areas to be covered and complexity in training, it brings the prospective benefits of MAL-DRL in the management of cloud-edge resources on the spot. In the course of this undertake, the effect of ensuring communication goals, moving learning strategies, and security measures for the ultimate goal of boosting this method's applicability in real-world antics can be explored 2024-03-15 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11795/1/P17003_0e3c560e211e3d06995d79b44427688c.pdf%206.pdf Ahmed Adhoni, Zameer and Habelalmateen, Mohammed and DR Janardhana, DR Janardhana and Abdul Sattar, Khalid Nazim and Audah, Lukman (2024) Deep reinforcement learning based resource allocation strategy in cloud-edge computing system. In: 2ND INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING AND OPTIMIZATION TECHNIQUES (ICDCOT). http://10.1109/ICICACS60521.2024.10498384 |
| spellingShingle | T Technology (General) Ahmed Adhoni, Zameer Habelalmateen, Mohammed DR Janardhana, DR Janardhana Abdul Sattar, Khalid Nazim Audah, Lukman Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title | Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title_full | Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title_fullStr | Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title_full_unstemmed | Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title_short | Deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| title_sort | deep reinforcement learning based resource allocation strategy in cloud-edge computing system |
| topic | T Technology (General) |
| url | http://eprints.uthm.edu.my/11795/1/P17003_0e3c560e211e3d06995d79b44427688c.pdf%206.pdf http://eprints.uthm.edu.my/11795/ http://10.1109/ICICACS60521.2024.10498384 |
| url_provider | http://eprints.uthm.edu.my/ |