Q-aware: Quality of service based cloud resource provisioning. (October 2015)

• Record Type: Journal Article
• Authors: Singh, Sukhpal; Chana, Inderveer
• Is Part Of: Computers & electrical engineering. Volume 47(2015)
• Page Start: 138
• Page End: 160
• Publication Date: 2015-10
• Subjects: Cloud metrics -- Workload patterns -- Resource provisioning -- Cloud computing -- Cloud workloads -- Quality of service; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.02.003
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract : Graphical abstract: Highlights: Cloud workloads have been analyzed and clustered through workload patterns. QoS metrics of each workload have been identified. We have analyzed the effect of number of workloads and resources on execution time and cost. Proposed technique demonstrates the minimization of cost and time simultaneously while adhering to workload deadline. Abstract: Provisioning of appropriate resources to cloud workloads depends on the Quality of Service (QoS) requirements of cloud workloads. Based on application requirements of cloud users, discovery and allocation of best workload – resource pair is an optimization problem. Acceptable QoS cannot be provided to the cloud users until provisioning of resources is offered as a crucial ability. QoS parameters based resource provisioning technique is therefore required for efficient provisioning of resources. In this paper, QoS metric based resource provisioning technique has been proposed. The proposed technique caters to provisioned resource distribution and scheduling of resources. The main aim of this research work is to analyze the workloads, categorize them on the basis of common patterns and then provision the cloud workloads before actual scheduling. The experimental results demonstrate that QoS metric based resource provisioning technique is efficient in reducing execution time and execution cost of cloud workloads along with other QoS parameters.