Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment
Abstract Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust p...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-11-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46284-9 |
| _version_ | 1797259295015829504 |
|---|---|
| author | Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali M. Ijaz Khan Dilsora Abduvalieva Fuad A. Awwad Emad A. A. Ismail |
| author_facet | Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali M. Ijaz Khan Dilsora Abduvalieva Fuad A. Awwad Emad A. A. Ismail |
| author_sort | Sudheer Mangalampalli |
| collection | DOAJ |
| description | Abstract Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters. |
| first_indexed | 2024-03-11T11:04:30Z |
| format | Article |
| id | doaj.art-8fee613167844f90a4516e11d87586ae |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T23:07:09Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8fee613167844f90a4516e11d87586ae2024-03-17T12:27:11ZengNature PortfolioScientific Reports2045-23222023-11-0113113210.1038/s41598-023-46284-9Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environmentSudheer Mangalampalli0Ganesh Reddy Karri1Sachi Nandan Mohanty2Shahid Ali3M. Ijaz Khan4Dilsora Abduvalieva5Fuad A. Awwad6Emad A. A. Ismail7School of Computer Science and Engineering, VIT-AP UniversitySchool of Computer Science and Engineering, VIT-AP UniversitySchool of Computer Science and Engineering, VIT-AP UniversityElectronics Engineering, Peking UniversityDepartment of Mathematics and Statistics, Riphah International University I-14Doctor of Philosophy in Pedagogical Sciences, Tashkent State Pedagogical UniversityDepartment of Quantitative Analysis, College of Business Administration, King Saud UniversityDepartment of Quantitative Analysis, College of Business Administration, King Saud UniversityAbstract Cloud Computing model provides on demand delivery of seamless services to customers around the world yet single point of failures occurs in cloud model due to improper assignment of tasks to precise virtual machines which leads to increase in rate of failures which effects SLA based trust parameters (Availability, success rate, turnaround efficiency) upon which impacts trust on cloud provider. In this paper, we proposed a task scheduling algorithm which captures priorities of all tasks, virtual resources from task manager which comes onto cloud application console are fed to task scheduler which takes scheduling decisions based on hybridization of both Harris hawk optimization and ML based reinforcement algorithms to enhance the scheduling process. Task scheduling in this research performed in two phases i.e. Task selection and task mapping phases. In task selection phase, all incoming priorities of tasks, VMs are captured and generates schedules using Harris hawks optimization. In task mapping phase, generated schedules are optimized using a DQN model which is based on deep reinforcement learning. In this research, we used multi cloud environment to tackle availability of VMs if there is an increase in upcoming tasks dynamically and migrate tasks to one cloud to another to mitigate migration time. Extensive simulations are conducted in Cloudsim and workload generated by fabricated datasets and realtime synthetic workloads from NASA, HPC2N are used to check efficacy of our proposed scheduler (FTTHDRL). It compared against existing task schedulers i.e. MOABCQ, RATS-HM, AINN-BPSO approaches and our proposed FTTHDRL outperforms existing mechanisms by minimizing rate of failures, resource cost, improved SLA based trust parameters.https://doi.org/10.1038/s41598-023-46284-9 |
| spellingShingle | Sudheer Mangalampalli Ganesh Reddy Karri Sachi Nandan Mohanty Shahid Ali M. Ijaz Khan Dilsora Abduvalieva Fuad A. Awwad Emad A. A. Ismail Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment Scientific Reports |
| title | Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment |
| title_full | Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment |
| title_fullStr | Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment |
| title_full_unstemmed | Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment |
| title_short | Fault tolerant trust based task scheduler using Harris Hawks optimization and deep reinforcement learning in multi cloud environment |
| title_sort | fault tolerant trust based task scheduler using harris hawks optimization and deep reinforcement learning in multi cloud environment |
| url | https://doi.org/10.1038/s41598-023-46284-9 |
| work_keys_str_mv | AT sudheermangalampalli faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT ganeshreddykarri faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT sachinandanmohanty faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT shahidali faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT mijazkhan faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT dilsoraabduvalieva faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT fuadaawwad faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment AT emadaaismail faulttoleranttrustbasedtaskschedulerusingharrishawksoptimizationanddeepreinforcementlearninginmulticloudenvironment |