Title: Resource allocation algorithm for GPUs in a private cloud

Authors: Ahmed Hosny Ibrahim; Hossam El-Deen Mostafa Faheem; Youssef Bassyouni Mahdy; Abdel-Rahman Hedar

Addresses: Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, Egypt ' Department of Computer Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt ' Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, Egypt ' Department of Computer Science, Faculty of Computers and Information, Assiut University, Assiut, Egypt

Abstract: Cloud computing is currently playing an important role in different applications. Deploying graphical processing units (GPUs) as a resource in clouds constitutes a major challenge. As the number and the scale of internet services increase, the services running on a cloud computing environment also increase. These services could have been efficiently improved in terms of a service level agreement (SLA) if we could make use of the GPUs architectures in the cloud infrastructure. Cloud providers currently offer GPUs as an infrastructure as a service (IaaS). Providing GPUs in another context such as software as a service (SaaS) requires a resource allocation algorithm. This paper proposes a resource allocation algorithm (RAA) for GPUs in the cloud environment. The proposed RAA will provide a way for cloud providers to offer GPUs in a SaaS context. The RAA is based on the load balancing mechanism trying to fully utilise all the GPUs resources. This paper also investigates the suitability of the RAA by conducting a video enhancement service on a private cloud. It shows that the proposed algorithm provides scalability and enhancements in terms of a SLA.

Keywords: graphical processing units; GPUs; cloud computing; resource allocation; cloud services; private cloud; service level agreements; SLAs; cloud infrastructure; software as a service; SaaS; video enhancement service.

DOI: 10.1504/IJCC.2016.075094

International Journal of Cloud Computing, 2016 Vol.5 No.1/2, pp.45 - 56

Received: 09 Oct 2014
Accepted: 11 Mar 2015

Published online: 03 Mar 2016 *

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