Name
Abstract | ||
|---|---|---|
AbstractCloud computing makes utility computing possible with pay as you go model. It virtualizes the systems by polling and sharing the resources, thus we need to handle more than one workflow at the same time. Workflow is the standard to represent compute intensive applications in scientific and engineering domain. Hence, in this article, the authors presented the scheduling heuristic for multiple workflows running parallel in the cloud environment with the aim to reduce the energy consumption as it is one of the major concerns of cloud data centers along with the execution performance. In the proposed approach, first clustering is performed to minimize the energy consumption and execution time during communication corresponding to precedence constraint tasks. Then cluster are scheduled is on the best available energy efficient resources. Finally, DVFS is applied in order to reduce energy consumption further when the nodes are in the idle and communication stage. The simulation has been performed on CloudSim and the results show the reduction in energy consumption by up to 42%. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.4018/IJITWE.2018070102 | Periodicals |
Keywords | Field | DocType |
Cloud Computing, Energy Consumption, Group Clustering, Makespan, Multiple Workflows, Scheduling, Voltage/Frequency Scaling | Computer science,Energy efficient scheduling,Workflow,Database,Cloud computing | Journal |
Volume | Issue | ISSN |
13 | 3 | 1554-1045 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| ritu garg | 1 | 6 | 4.50 |
| Neha Shukla | 2 | 0 | 0.68 |