Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment
Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2022/10/itmconf_icaect2022_01004.pdf |
| _version_ | 1828062241629405184 |
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| author | Lahande Prathamesh Kaveri Parag |
| author_facet | Lahande Prathamesh Kaveri Parag |
| author_sort | Lahande Prathamesh |
| collection | DOAJ |
| description | Cloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance. |
| first_indexed | 2024-04-10T22:26:33Z |
| format | Article |
| id | doaj.art-b1bf7f4fc0af44e687c9c068433cd1bc |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-04-10T22:26:33Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-b1bf7f4fc0af44e687c9c068433cd1bc2023-01-17T09:14:47ZengEDP SciencesITM Web of Conferences2271-20972022-01-01500100410.1051/itmconf/20225001004itmconf_icaect2022_01004Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud EnvironmentLahande Prathamesh0Kaveri Parag1Symbiosis Institute of Computer Studies and ResearchSymbiosis Institute of Computer Studies and ResearchCloud has grown significantly and has become a popular serviceoriented paradigm offering users a variety of services. The end-user submits requests to the cloud in the form of tasks with the expectation that they will be executed at the best possible lowest time, cost and without any errors. On the other hand, the cloud executes these tasks on the Virtual Machines (VM) by using resource scheduling algorithms. The cloud performance is directly dependent on how the resources are managed and allocated for executing the tasks. The main aim of this research paper is to compare the behaviour of cloud resource scheduling algorithms: First Come First Serve (FCFS) and Shortest Job First (SJF) by processing high-sized tasks. This research paper is broadly divided into four phases: the first phase includes an experiment conducted by processing approximately 80 thousand tasks from the Alibaba task event dataset using the resource scheduling algorithms: FCFS and SJF on the cloud VMs under different circumstances; the second phase includes the experimental results; the third phase includes a empirical analysis of the behaviour of resource scheduling algorithms; the last phase includes the proposed need of Reinforcement Learning (RL) to improve cloud resource scheduling and its overall performance.https://www.itm-conferences.org/articles/itmconf/pdf/2022/10/itmconf_icaect2022_01004.pdf |
| spellingShingle | Lahande Prathamesh Kaveri Parag Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment ITM Web of Conferences |
| title | Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment |
| title_full | Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment |
| title_fullStr | Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment |
| title_full_unstemmed | Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment |
| title_short | Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment |
| title_sort | implementing fcfs and sjf for finding the need of reinforcement learning in cloud environment |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2022/10/itmconf_icaect2022_01004.pdf |
| work_keys_str_mv | AT lahandeprathamesh implementingfcfsandsjfforfindingtheneedofreinforcementlearningincloudenvironment AT kaveriparag implementingfcfsandsjfforfindingtheneedofreinforcementlearningincloudenvironment |