3 research outputs found
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), 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.peer-reviewe