R&D: SLA-Aware Resource Scheduling Algorithm for Cloud Storage

EURASIP Journal on Wireless Communications and Networking has published an article written by Yong Wang, Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin, China, and Guangxi Cooperative Innovation Center of cloud computing and Big Data, Guilin University of Electronic Technology, Guilin, China, Xiaoling Tao, Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin, China, State Key Laboratory of Integrated Service Networks (ISN), Xidian University, Xi’an, China, and Guangxi Cooperative Innovation Center of cloud computing and Big Data, Guilin University of Electronic Technology, Guilin, China, Feng Zhao, Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin, China, BoTian, Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems, Guilin University of Electronic Technology, Guilin, China, and Guangxi Cooperative Innovation Center of cloud computing and Big Data, Guilin University of Electronic Technology, Guilin, China, and Akshita Maradapu Vera Venkata Sai, Department of Computer Science Georgia State University, Atlanta, USA.

Abstract: “Cloud computing is a novel computing paradigm, which connects plenty of computing resources and storage resources via Internet. Cloud computing provides a number of high-quality services, such as cloud storage, outsourcing computing, and on-demand self-service, which have been widely accepted by the public. In cloud computing, by submitting their tasks to cloud, plenty of applications share huge computation and storage resources. However, how to schedule resource efficiently is a big challenge in cloud computing.In this paper, we propose a SLA-aware resource algorithm to enable cloud storage more efficiently. In our scheme, we take advantage of the back-end node space utilization and I/O throughput comprehensively simultaneously. We compare and contrast the existing scheduling storage policies by implementing those algorithms. The extensive tests show that our algorithm achieves a considerable improvement in terms of violation rate and the number of used hosts.“