Title: Design and implementation of a framework for provisioning algorithms as a service

Authors: Abdullah A. Qasem; Qusay H. Mahmoud

Addresses: Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canada ' Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canada

Abstract: Designing, implementing and executing algorithms have become a relevant and important element in various fields. Public users and data researchers are interested in analysing and interpreting data with shorter execution time and higher performance. Cloud computing is an environment that provides scalable and high-end virtual resources to achieve high quality services. This paper presents the design, implementation and evaluation of a framework for provisioning algorithms as a service in the cloud. This framework introduces solutions to help clients overcome different concerns and difficulties, such as looking for an appropriate algorithm, understanding algorithm source code, installing and configuring specific libraries, and achieving high algorithmic performance. The framework provides clients the possibility to discover available algorithms and/or deploy new algorithms over multiple scalable platforms. It also allows clients to analyse data, compare results, and measure algorithm's performance. A prototype implementation of the framework has been developed to demonstrate the feasibility of the solution. Evaluating results demonstrate that providing multiple scalability models and high-end web servers will improve algorithm performance and achieve availability and reliability using the framework.

Keywords: algorithms as a service; AaaS; Amazon Web Services; AWS; cloud computing; software as a service; SAAS; scalability models; sequential and parallel algorithms.

DOI: 10.1504/IJCC.2017.086713

International Journal of Cloud Computing, 2017 Vol.6 No.3, pp.265 - 288

Available online: 19 Sep 2017

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