Cloud Computing
S. M. Hashemi; A. Sahafi; A. M. Rahmani; M. Bohlouli
Abstract
Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have ...
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Background and Objectives: Today, the increased number of Internet-connected smart devices require powerful computer processing servers such as cloud and fog and necessitate fulfilling requests and services more than ever before. The geographical distance of IoT devices to fog and cloud servers have turned issues such as delay and energy consumption into major challenges. However, fog computing technology has emerged as a promising technology in this field.Methods: In this paper, service/energy management approaches are generally surveyed. Then, we explain our motivation for the systematic literature review procedure (SLR) and how to select the related works.Results: This paper introduces four domains of service management and energy management, including Architecture, Resource Management, Scheduling management, and Service Management. Scheduling management has been used in 38% of the papers. Therefore, they have the highest service management and energy management. Also, Resource Management is the second domain that has been able to attract about 26% of the papers in service management and energy management.Conclusion: About 81% of the fog computing papers simulated their approaches, and the others implemented their schemes using a testbed in the real environment. Furthermore, 30% of the papers presented an architecture or framework for their research, along with their research. In this systematic literature review, papers have been extracted from five valid databases, including IEEE Xplore, Wiley, Science Direct (Elsevier), Springer Link, and Taylor & Francis, from 2013 to 2022. We obtained 1596 papers related to the discussed subject. We filtered them and achieved 47 distinct studies. In the following, we analyze and discuss these studies; then we review the parameters of service quality in the papers, and ultimately, we present the benefits, drawbacks, and innovations of each study.
Cloud Computing
M. Farmani; S. Farnam; M. J. Khani; Z. Torabi; Z. Shirmohammadi
Abstract
Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of ...
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Background and Objectives: With the increase of population in the world along with the decrease of natural resources, agricultural land and the increase of unpredictable environmental conditions, it causes concerns in the field of food supply, which is one of the serious concerns for all countries of the world. Therefore, the agricultural industry has moved towards smart agriculture. Smart agriculture using the Internet of Things, which uses different types of sensors to collect data (such as temperature, humidity, light, etc.), a communication network to send and receive data, and information systems to manage and analyze data. Smart agriculture deals with a huge amount of data collected from farms, which has fundamental challenges for analysis using old systems such as lack of storage space, processing delay. Computational paradigm is a key solution to solve the problems of time delay, security, storage space management, real-time analysis. Computing paradigms include cloud, fog and edge computing, which by combining each of them in smart agriculture has caused a great transformation in this industry. The purpose of this article is to provide a comprehensive review of the architecture of computing paradigms in smart agriculture applications.Methods: To achieve the goals of this article, the methodology is divided into two parts: article selection and review of the selected articles. The computational paradigms used in the selected articles are from 2019 to 2022. Each selected paper is then reviewed in detail in terms of categories of computing paradigms, architectures, key points, advantages, and challenges.Results: Computational paradigms have significant advantages. Combining these paradigms with each other in a complementary way covers many challenges. The architecture based on the combination of edge-fog-cloud computing is one of the best architectures combined with smart agriculture.Conclusion: By combining computing paradigms and smart agriculture, the challenges based on traditional and old systems are overcome. Combining these paradigms complement each other's challenges.
Cloud Computing
H. KardanMoghaddam; A. Rajaei; F. Jafari
Abstract
Background and Objectives: Determining effective factors in cloud computing adoption on employees of Noor credit institution in South Khorasan province, Iran, is the purpose of the present study. A practical oriented method is applied regarding the research objective and a descriptive-survey method is ...
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Background and Objectives: Determining effective factors in cloud computing adoption on employees of Noor credit institution in South Khorasan province, Iran, is the purpose of the present study. A practical oriented method is applied regarding the research objective and a descriptive-survey method is used for collecting field data. Employees of Noor credit institution of South Khorasan province (50 people) are selected as the research sample.Methods: Accurate questionnaires are analyzed. Two researcher-made questionnaires are conducted as research tools. These questionnaires consist of effective factors in cloud computing adoption (12 factors including 47 items) and cloud computing adoption intention (3 items). The reliability of the research is evaluated using Cronbach's alpha coefficient which is obtained α=0.54% for the questionnaire of effective factors in cloud computing adoption and has various values for the questionnaire of the cloud computing adoption intention. Also, the descriptive statistics approach is used to define variables, and confirmatory factor analysis and path analysis are applied in the inferential section. Structural equation modeling using Smart-PLS software is used to determine the effective factors in cloud computing adoptionResults: All 12 determined factors in cloud computing adoption are considered as dependent variables and cloud computing adoption intention is considered as an independent variable. 12 effective factors in cloud computing adoption on Noor credit institution of South Khorasan in Iran are determined and examined.Conclusion: It can be concluded that all factors (except the support of the top manager) have a positive and significant effect on cloud computing adoption intention, nonetheless, the value of statistic t for path analysis of the support of the top manager on cloud computing adoption intention is less than 1.96. This shows that the support of the top manager does not have a significant effect on cloud computing adoption intention.
Cloud Computing
H. Jahanpour; H. Barati; A. Mehranzadeh
Abstract
Background and Objectives: Cloud Computing has brought a new dimension to the IT world. The technology of cloud computing allows employing a large number of Virtual Machines to run intensive applications. Each failure in running applications fails system operations. To solve the problem, it is required ...
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Background and Objectives: Cloud Computing has brought a new dimension to the IT world. The technology of cloud computing allows employing a large number of Virtual Machines to run intensive applications. Each failure in running applications fails system operations. To solve the problem, it is required to restart the systems.Methods: In this paper, to predict and avoid failure in HPC systems, a method of fault tolerance to High-Performance Computing systems (HPC) in the cloud is called Daemon-COA-MMT (DCM), has been proposed. In the proposed method, the Daemon Fault Tolerance technique has been enhanced, and COA-MMT has been utilized for load balancing. The method consists of four modules, which are used to determine the host state. When the system is in the alarm state, the current host may face failure. Then the most optimal host for migration is selected, and process-level migration is performed. The method causes decreased migration overheads, decreased system performance speed, optimal use of underutilized hosts instead of leasing new hosts, appropriate load balancing, equal use of hardware resources by all hosts, focusing on QoS and SLA, and the significant decrease of energy consumption.Results: The simulation results revealed that in terms of parameters, the proposed method declines average job makespan, average response time, and average task execution cost by 18.06%, 35.68%, and 24.6%, respectively. The proposed fault tolerance algorithm has improved energy consumption by 30% and decreased the HPC systems' failure rate.Conclusion: In this study, the Daemon Fault Tolerance technique has been enhanced, and COA-MMT has been utilized for load balancing in high performance computing in the cloud computing.
