Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing
Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), m...
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doaj-727522e416524de6ada1189f645335172021-06-01T01:46:01ZengMDPI AGElectronics2079-92922021-05-01101320132010.3390/electronics10111320Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud ComputingVijay Prakash0Seema Bawa1Lalit Garg2Department of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala 147001, IndiaDepartment of Computer Science & Engineering, Thapar Institute of Engineering & Technology, Patiala 147001, IndiaComputer Information Systems, Faculty of Information & Communication Technology, University of Malta, MSD 2080 Msida, MaltaWorkflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.https://www.mdpi.com/2079-9292/10/11/1320workflow managementworkflow schedulingscientific applicationscloud computingworkflowSimMaxChild and Scheduling Algorithms |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vijay Prakash Seema Bawa Lalit Garg |
spellingShingle |
Vijay Prakash Seema Bawa Lalit Garg Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing Electronics workflow management workflow scheduling scientific applications cloud computing workflowSim MaxChild and Scheduling Algorithms |
author_facet |
Vijay Prakash Seema Bawa Lalit Garg |
author_sort |
Vijay Prakash |
title |
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing |
title_short |
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing |
title_full |
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing |
title_fullStr |
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing |
title_full_unstemmed |
Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing |
title_sort |
multi-dependency and time based resource scheduling algorithm for scientific applications in cloud computing |
publisher |
MDPI AG |
series |
Electronics |
issn |
2079-9292 |
publishDate |
2021-05-01 |
description |
Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources. |
topic |
workflow management workflow scheduling scientific applications cloud computing workflowSim MaxChild and Scheduling Algorithms |
url |
https://www.mdpi.com/2079-9292/10/11/1320 |
work_keys_str_mv |
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