Journal
2018 11TH INTERNATIONAL CONFERENCE ON THE QUALITY OF INFORMATION AND COMMUNICATIONS TECHNOLOGY (QUATIC)
Volume -, Issue -, Pages 210-219Publisher
IEEE
DOI: 10.1109/QUATIC.2018.00039
Keywords
Big Data Quality; Big Data; Cloud scheduling; Dynamic cloud scheduling; Cloud Computing
Funding
- VPPI - University of Sevilla
- COST Action [IC1406]
Ask authors/readers for more resources
The quality of services in Cloud Computing (CC) depends on the scheduling strategies selected for processing of the complex workloads in the physical cloud clusters. Using the scheduler of the single type does not guarantee of the optimal mapping of jobs onto cloud resources, especially in the case of the processing of the big data workloads. In this paper, we compare the performances of the cloud schedulers for various combinations of the cloud workloads with different characteristics. We define several scenarios where the proper types of schedulers can be selected from a list of scheduling models implemented in the system, and used to schedule the concrete workloads based on the workloads' parameters and the feedback on the efficiency of the schedulers. The presented work is the first step in the development and implementation of an automatic intelligent scheduler selection system. In our simple experimental analysis, we confirm the usefulness of such a system in today's data-intensive cloud computing.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available