Diversified Quality Aware Ensemble Resource Scheduling (DQAERS) for IAAS with Massive Load of Tasks and Resources in Cloud Computing
B. Ravindra Babu1, M. Veera Sekhar Rao2
1B. Ravindra Babu, Research Scholar, JNTUH, Hyderabad, (Telangana), India.
2M. Veera Sekhar Rao, Research Scholar, JNTUH, Hyderabad, (Telangana), India. 

Manuscript received on February 02, 2019. | Revised Manuscript received on February 14, 2019. | Manuscript published on August 30, 2019. | PP: 3758-3762 | Volume-8 Issue-6, August 2019. | Retrieval Number: F9386088619/19©BEIESP | DOI: 10.35940/ijeat.F9386.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Identifying a deterministic approach to perform resource scheduling in cloud computing is crucial requirement, which is since, the volume of the anomalies and the high dimensionality of the values projected to these anomalies observed during resource scheduling. The volume of tasks that evinces flash-crowd state at resource broker of the IAAS, and high dimensionality of the anomalies projected for resource quality factors are out of scope in regard to contemporary resource scheduling strategies contributed in recent past. Hence’ the resource scheduling by contemporary methods in such conditions are insignificant as the resource scheduling optimality observed as probabilistic. In order to optimize the resource scheduling in the context of aforesaid properties high volume of tasks (flash-crowd state at resource broker) and high dimensional projection of anomalies, this manuscript derived an ensemble resource scheduling strategy, which fall in to the category of batch scheduling. The experimental study outlined that the proposal is most prominent and robust to deliver optimal resource scheduling in the context of anomalies of high volume and dimensionality that compared to the contemporary method.
Keywords: Cloud computing (CC), Resource scheduling (Resource Scheduling), Virtual machines (VMs), VM migration, Resource management (RM), QoS (Quality of Services).