Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment
Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provi...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10614579/ |
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| author | S. Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali Atif M. Alamri Salman A. Alqahtani |
| author_facet | S. Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali Atif M. Alamri Salman A. Alqahtani |
| author_sort | S. Sudheer Mangalampalli |
| collection | DOAJ |
| description | Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provider(CSP). Therefore, to generate task schedules in cloud paradigm effectively by considering type of task arising to cloud console and match it with respective Virtual Machine (VM), a task scheduler is formulated by using Deep Deterministic Policy Gradient (DDPG) algorithm which is used as methodology to design scheduler. This scheduler works in three stages. In the initial stage, tasks are classified based on length and processing capacity to identify them whether they are High Performance Computing (HPC) tasks or High Throughput Computing (HTC) tasks. After classification, in the second stage, resources are to be tracked which matches the corresponding nature of tasks. Finally, in the third stage, according to the VM priorities calculated based on electricity unit cost and tasks are mapped according to the priorities to the corresponding VMs. Simulations are conducted using Cloudsim with fabricated workload distributions and realtime worklogs. Finally, our proposed Hybrid workload Deep Deterministic Policy Gradient Task scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, it proved that our proposed HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability over baseline approaches. |
| format | Article |
| id | doaj-art-1c878139858f42f88e7accdbce862e65 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1c878139858f42f88e7accdbce862e652025-08-20T02:40:02ZengIEEEIEEE Access2169-35362024-01-011210889710892010.1109/ACCESS.2024.343591410614579Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud EnvironmentS. Sudheer Mangalampalli0Ganesh Reddy Karri1Sachi Nandan Mohanty2Shahid Ali3https://orcid.org/0009-0007-0731-0799Atif M. Alamri4Salman A. Alqahtani5Department of Computer Science and Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravati, IndiaSchool of Computer Science and Engineering, VIT-AP University, Amaravati, IndiaSchool of Electronics, Peking University, Beijing, ChinaSoftware Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaComputer Engineering Department, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaTask Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provider(CSP). Therefore, to generate task schedules in cloud paradigm effectively by considering type of task arising to cloud console and match it with respective Virtual Machine (VM), a task scheduler is formulated by using Deep Deterministic Policy Gradient (DDPG) algorithm which is used as methodology to design scheduler. This scheduler works in three stages. In the initial stage, tasks are classified based on length and processing capacity to identify them whether they are High Performance Computing (HPC) tasks or High Throughput Computing (HTC) tasks. After classification, in the second stage, resources are to be tracked which matches the corresponding nature of tasks. Finally, in the third stage, according to the VM priorities calculated based on electricity unit cost and tasks are mapped according to the priorities to the corresponding VMs. Simulations are conducted using Cloudsim with fabricated workload distributions and realtime worklogs. Finally, our proposed Hybrid workload Deep Deterministic Policy Gradient Task scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, it proved that our proposed HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability over baseline approaches.https://ieeexplore.ieee.org/document/10614579/Task schedulingcloud computingmakespanenergy consumptionDQNDDPG |
| spellingShingle | S. Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali Atif M. Alamri Salman A. Alqahtani Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment IEEE Access Task scheduling cloud computing makespan energy consumption DQN DDPG |
| title | Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment |
| title_full | Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment |
| title_fullStr | Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment |
| title_full_unstemmed | Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment |
| title_short | Efficient Hybrid DDPG Task Scheduler for HPC and HTC in Cloud Environment |
| title_sort | efficient hybrid ddpg task scheduler for hpc and htc in cloud environment |
| topic | Task scheduling cloud computing makespan energy consumption DQN DDPG |
| url | https://ieeexplore.ieee.org/document/10614579/ |
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