Authors: Jamilson Dantas; Eltton Araujo; Paulo Maciel; Rubens Matos; Jean Teixeira
Addresses: Informatics Center, Federal University of Pernambuco, Recife, Brazil ' Informatics Center, Federal University of Pernambuco, Recife, Brazil ' Informatics Center, Federal University of Pernambuco, Recife, Brazil ' Federal Institute of Education, Science, and Technology, Lagarto, Brazil ' Federal Rural University of Pernambuco, Garanhuns, Brazil
Abstract: Over the years, many companies have employed cloud computing to support their services and optimise their infrastructure utilisation. The provisioning of high availability and high processing capacity is a significant challenge when planning a cloud computing infrastructure. Even when the system is available, a part of the resources may not be offered due to partial failures in just a few of the many components in an IaaS cloud. The dynamic behaviour of virtualised resources requires special attention to the effective amount of capacity that is available to users, so the system can be correctly sized. Therefore, the estimation of capacity-oriented availability (COA) is an important activity for cloud infrastructure providers to analyse the cost-benefit tradeoff among distinct architectures and deployment sizes. This paper presents a strategy to evaluate the capacity-oriented availability of virtual machines combined to servers availability on a private cloud infrastructure. The proposed strategy aims to provide an efficient and accurate computation of COA and availability by means of closed-form equations. We compare our approach to the use of models such as continuous time Markov chains and SPN simulation model, considering execution time and values of metrics obtained with both approaches.
Keywords: capacity-oriented availability; COA; closed-form equation; cloud computing; continuous time Markov chain; CTMC.
International Journal of Computational Science and Engineering, 2020 Vol.22 No.4, pp.466 - 476
Received: 06 Sep 2019
Accepted: 14 Nov 2019
Published online: 08 Sep 2020 *