Quality of cloud services determined by the dynamic management of scheduling models for complex heterogeneous workloads

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.