Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment
Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed...
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| Published in: | The Journal of supercomputing Vol. 77; no. 8; pp. 8252 - 8280 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
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Springer US
01.08.2021
Springer Nature B.V |
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| ISSN: | 0920-8542, 1573-0484 |
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| Abstract | Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation. |
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| AbstractList | Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating the number of virtual machines needed in the near future along with their predicted CPU and memory requirements, which is the main contribution of the work. K-means algorithm clusters the tasks based on CPU and memory usage as parameters. The future arrival of tasks for every cluster is predicted and accordingly, the required number of VMs is created. The incoming requests known as tasks are scheduled on the appropriate VM using the genetic algorithm (GA). Based on the workload prediction results, a cost-optimized resource scheduling strategy in cloud computing environment is proposed aiming at minimizing the total cost of rental virtual machines from the central cloud. Finally, a genetic algorithm is used to solve the resource scheduling strategy. The developed algorithms are evaluated by the workload prediction accuracy, the total cost of the cluster and the algorithm’s consuming time for solving the resource scheduling problems through the experiments. Finally, the effective of workload prediction algorithm based on SES and cost-optimized resource scheduling strategy is verified by simulation. |
| Author | Devi, K. Lalitha Valli, S. |
| Author_xml | – sequence: 1 givenname: K. Lalitha orcidid: 0000-0003-0284-3522 surname: Devi fullname: Devi, K. Lalitha email: lalithavelu10@gmail.com organization: Department of Computer Science and Engineering, College of Engineering, Guindy Campus, Anna University – sequence: 2 givenname: S. surname: Valli fullname: Valli, S. organization: Department of Computer Science and Engineering, College of Engineering, Guindy Campus, Anna University |
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| Cites_doi | 10.1016/j.future.2016.02.016 10.1109/TPDS.2012.283 10.1109/12.817403 10.1177/0020720919894199 10.1016/j.asoc.2014.01.036 10.1109/TNSM.2015.2436408 10.1016/j.cor.2013.06.012 10.1016/j.jnca.2019.06.006 10.1016/j.ins.2014.02.122 10.3844/jcssp.2011.877.883 10.1002/dac.4596 10.1109/TNSM.2013.051913.120278 10.1007/s11227-018-2669-y 10.1016/j.jnca.2018.03.028 10.1109/TASE.2017.2693688 10.1109/TCC.2014.2306427 10.1371/journal.pone.0176321 10.1109/ACCESS.2019.2948704 10.1016/j.jss.2016.07.006 10.1007/s11227-019-03134-8 10.1007/s11227-019-02748-2ci 10.1109/TPDS.2015.2446459 10.1007/s11227-020-03163-8 10.1007/s11227-020-03305-y 10.1109/ICEEOT.2016.7755364 10.1109/ICECA.2019.8822020 10.1109/CICN.2014.128 10.1109/CCIS.2011.6045063 10.1145/2391229.2391236 10.6084/m9.figshare.4877438.v2 10.1145/2656075.2656095 10.1109/CICN.2014.220 10.1109/DCABES.2014.18 |
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| Keywords | Task scheduling Cloud computing Genetic algorithm Data clustering Workload prediction |
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| SubjectTerms | Central processing units Cloud computing Clusters Compilers Computer memory Computer Science CPUs Genetic algorithms Interpreters Multiple objective analysis Processor Architectures Programming Languages Resource scheduling Schedules Scheduling Task scheduling Virtual environments Workload Workloads |
| Title | Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment |
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| Volume | 77 |
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