Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment
Machine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9833517/ |
| _version_ | 1818492748019269632 |
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| author | Arabinda Pradhan Sukant Kishoro Bisoy Sandeep Kautish Muhammed Basheer Jasser Ali Wagdy Mohamed |
| author_facet | Arabinda Pradhan Sukant Kishoro Bisoy Sandeep Kautish Muhammed Basheer Jasser Ali Wagdy Mohamed |
| author_sort | Arabinda Pradhan |
| collection | DOAJ |
| description | Machine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel processing algorithms like Parallel Particle Swarm Optimization (PPSO) are parallelized to speed up the operation and reduce the processing time to train the neural network. Due to the arrival of a large number of incoming tasks in the cloud environment, load balancing is an important issue. To solve this problem, the datacenter controller or an agent makes an intelligent decision to handle a large number of tasks within a minimum time period or at high speed. In this work, we proposed an effective scheduling algorithm named Deep Reinforcement Learning with Parallel Particle Swarm Optimization (DRLPPSO) to solve the load balancing problem and its various parameters with greater accuracy and high speed. Our experimental results show that our proposed scheduling algorithm increases the reward by 15.7%, 12%, and 13.1% when the task set is 2000 and improves the reward by 17.5%, 12.6%, and 15.3% when the task set is 4000, as compared to the Modified Particle Swarm Optimization (MPSO), Asynchronous Advantage Actor-Critic (A3C), and Deep Q-Network (DQN) techniques. |
| first_indexed | 2024-12-10T17:47:05Z |
| format | Article |
| id | doaj.art-bf4ac4f463ae46e098eb7c55ff1c7312 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T17:47:05Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf4ac4f463ae46e098eb7c55ff1c73122022-12-22T01:39:12ZengIEEEIEEE Access2169-35362022-01-0110769397695210.1109/ACCESS.2022.31926289833517Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud EnvironmentArabinda Pradhan0Sukant Kishoro Bisoy1https://orcid.org/0000-0002-2657-5799Sandeep Kautish2https://orcid.org/0000-0001-5120-5741Muhammed Basheer Jasser3https://orcid.org/0000-0001-5292-465XAli Wagdy Mohamed4https://orcid.org/0000-0002-5895-2632Department of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaDepartment of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, IndiaLBEF Campus Kathmandu, Kathmandu, NepalDepartment of Computing and Information Systems, School of Engineering and Technology, Sunway University, Petaling Jaya, MalaysiaOperations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, EgyptMachine learning and parallel processing are extremely commonly used to enhance computing power to induce knowledge from an outsized volume of data. To deal with the problem of complexity and high dimension, machine learning algorithms like Deep Reinforcement Learning (DRL) are used, while parallel processing algorithms like Parallel Particle Swarm Optimization (PPSO) are parallelized to speed up the operation and reduce the processing time to train the neural network. Due to the arrival of a large number of incoming tasks in the cloud environment, load balancing is an important issue. To solve this problem, the datacenter controller or an agent makes an intelligent decision to handle a large number of tasks within a minimum time period or at high speed. In this work, we proposed an effective scheduling algorithm named Deep Reinforcement Learning with Parallel Particle Swarm Optimization (DRLPPSO) to solve the load balancing problem and its various parameters with greater accuracy and high speed. Our experimental results show that our proposed scheduling algorithm increases the reward by 15.7%, 12%, and 13.1% when the task set is 2000 and improves the reward by 17.5%, 12.6%, and 15.3% when the task set is 4000, as compared to the Modified Particle Swarm Optimization (MPSO), Asynchronous Advantage Actor-Critic (A3C), and Deep Q-Network (DQN) techniques.https://ieeexplore.ieee.org/document/9833517/Load balancingdeep reinforcement learningneural networkparallel PSO |
| spellingShingle | Arabinda Pradhan Sukant Kishoro Bisoy Sandeep Kautish Muhammed Basheer Jasser Ali Wagdy Mohamed Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment IEEE Access Load balancing deep reinforcement learning neural network parallel PSO |
| title | Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment |
| title_full | Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment |
| title_fullStr | Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment |
| title_full_unstemmed | Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment |
| title_short | Intelligent Decision-Making of Load Balancing Using Deep Reinforcement Learning and Parallel PSO in Cloud Environment |
| title_sort | intelligent decision making of load balancing using deep reinforcement learning and parallel pso in cloud environment |
| topic | Load balancing deep reinforcement learning neural network parallel PSO |
| url | https://ieeexplore.ieee.org/document/9833517/ |
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