Raja Masadeh, Nesreen Alsharman, Ahmad Sharieh, Basel A. Mahafzah and Arafat Abdulrahman
Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons…
Abstract
Purpose
Sea Lion Optimization (SLnO) algorithm involves the ability of exploration and exploitation phases, and it is able to solve combinatorial optimization problems. For these reasons, it is considered a global optimizer. The scheduling operation is completed by imitating the hunting behavior of sea lions.
Design/methodology/approach
Cloud computing (CC) is a type of distributed computing, contributory in a massive number of available resources and demands, and its goal is sharing the resources as services over the internet. Because of the optimal using of these services is everlasting challenge, the issue of task scheduling in CC is significant. In this paper, a task scheduling technique for CC based on SLnO and multiple-objective model are proposed. It enables decreasing in overall completion time, cost and power consumption; and maximizes the resources utilization. The simulation results on the tested data illustrated that the SLnO scheduler performed better performance than other state-of-the-art schedulers in terms of makespan, cost, energy consumption, resources utilization and degree of imbalance.
Findings
The performance of the SLnO, Vocalization of Whale Optimization Algorithm (VWOA), Whale Optimization Algorithm (WOA), Grey Wolf Optimization (GWO) and Round Robin (RR) algorithms for 100, 200, 300, 400 and 500 independent cloud tasks on 8, 16 and 32 VMs was evaluated. The results show that SLnO algorithm has better performance than VWOA, WOA, GWO and RR in terms of makespan and imbalance degree. In addition, SLnO exhausts less power than VWOA, WOA, GWO and RR. More precisely, SLnO conserves 5.6, 21.96, 22.7 and 73.98% energy compared to VWOA, WOA, GWO and RR mechanisms, respectively. On the other hand, SLnO algorithm shows better performance than the VWOA and other algorithms. The SLnO algorithm's overall execution cost of scheduling the cloud tasks is minimized by 20.62, 39.9, 42.44 and 46.9% compared with VWOA, WOA, GWO and RR algorithms, respectively. Finally, the SLnO algorithm's average resource utilization is increased by 6, 10, 11.8 and 31.8% compared with those of VWOA, WOA, GWO and RR mechanisms, respectively.
Originality/value
To the best of the authors’ knowledge, this work is original and has not been published elsewhere, nor is it currently under consideration for publication elsewhere.
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Janset Shawash, Noor Marji and Narmeen Marji
As the Hashemite Kingdom of Jordan celebrates its first centenary, this paper presents a critical reading of the development of architecture in the Kingdom reflecting the…
Abstract
Purpose
As the Hashemite Kingdom of Jordan celebrates its first centenary, this paper presents a critical reading of the development of architecture in the Kingdom reflecting the transformation of national identity.
Design/methodology/approach
The paper achieves this aim by performing an analytical diachronic survey of the main architectural styles and trends that emerged in Jordan and links the architectural styles and trends to four main historical periods that characterize the national temporal trajectory, supported by examples of buildings, projects and architects that represent each period.
Findings
The results show the impacts of different forms of architectural modernism on local practice and explore attempts to create a national architectural identity that range in their ideological drive from Pan-Arabism to Jordanian localism.
Originality/value
The research adds to the discourse on Arab cities and architecture and shows the development of architectural trends in an Arab Muslim country, focusing on the interaction of architectural modernism with local variables. The research aims to supplement literature on Arab architecture with a critical and nuanced historical account of Jordanian architecture in the English language to serve a global audience.
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Massimiliano Apolloni, Michael Volgger and Christof Pforr
As net-zero pledges gain momentum globally, more and more accommodation businesses seek to quantify their carbon emissions. Building on Chan (2021), this study aims to explore…
Abstract
Purpose
As net-zero pledges gain momentum globally, more and more accommodation businesses seek to quantify their carbon emissions. Building on Chan (2021), this study aims to explore what drives Australian accommodation providers to measure the carbon footprint of their businesses and what barriers hinder them from doing so.
Design/methodology/approach
Empirical data were collected by conducting ten semi-structured interviews with owners, senior executives, consultants, certification bodies and hotel management companies. The set of interviews represented different segments of the hotel industry and various accommodation types. Data were analysed with thematic analysis.
Findings
The major drivers for adopting carbon footprint analysis are as follows: the analysis being perceived as an important contribution to a company's corporate responsibility, the owner or manager's environmental concern, the assessment being a requirement for obtaining an eco-certification and the business benefits associated with implementing the initiative. The major barriers hindering adoption include the following: difficulties with data gathering, the lack of a standard methodology, a lengthy decision-making process and a lack of resources.
Research limitations/implications
Based on the empirical findings and three theories on ecological responsiveness, this study develops a conceptual framework for implementing carbon footprint analysis in the accommodation context and recommends strategies to increase the adoption of carbon footprint analysis.
Originality/value
This study responds to Chan and Hsu's (2016) call for further research on carbon footprint in the hotel context and represents the first attempt to explore the drivers and barriers specifically associated with implementing carbon footprint analysis in the accommodation sector.