Figure 6 - available via license: Creative Commons Attribution-NoDerivatives 4.0 International
Content may be subject to copyright.
Source publication
"Cloud" computing - a relatively recent term, builds on decades of research in virtualization, distributed computing, utility computing, and more recently networking, web and software services. It implies a service oriented architecture, reduced information technology overhead for the end-user, great flexibility, reduced total cost of ownership, on...
Context in source publication
Context 1
... our case, we can, and do, move our cloud resources from single seat environment to HPC environments, and vice versa, as the interest in one wanes and in the other one increases on holiday and semester boundaries. Figure 6 shows utilization of the VCL seatoriented resources by day over the last 4 years. We see the growth in usage, but we also see seasonal and semestral variations in utilization that invite re-targeting of the resources. ...
Citations
... This paper explored the cloud computing concept and related it with the old concept grid computing. Mladen A. Vouk_et al. [1] focuses on different existing platforms of cloud and explained the characteristics and applications of them. Different issues of cloud computing are pinpointed. ...
As the number of patients treated in-home are increasing exponentially mainly in countries such as Japan, US A and Europe etc., these people often enter into a critical situation that may require help immediately (e.g. when facing an accident, or becoming depressed). Researches and Advances in computing areas and the Internet of Things (IOT) have provided efficient and cheap equipments including wireless communication and cameras, such as smart phones or embedded devices that enable the deployment of Health S mart Homes (HS H) that can provide medical treatment of patients in their homes. The images captured from these cameras can help nurses or caretakers of patients to provide timely help. The use of such patient images and emotional detection to assist patients and elderly people within home is provided in this article. Internet of Things and Cloud computing can work together, which can address to the Big Data problems. Big data is actually a term used for a huge data set that performs operations like storage of data, analysis, sharing, transfer, predictive analysis; updating etc. data set can grow rapidly. Data sets have different analytics on data that involve process of inspecting, transformation and modeling of data so as to discover new information, and to reach on some particular decision. There are some particular data analysis techniques from which data mining is a popular one which focuses on modeling and discovery of new facts. The discovered knowledge helps in predictive analysis purposes as well as text analysis. Further on data mining is performed on data set which involves discovery of patterns by applying different operations at databases, and using artificial intelligence, machine learning, statics etc.
... According to Vouk (2008), cloud computing is built on virtualisation, distributed computing, grid computing, utility computing, networking, and web and software services. In more general terms, Buyya et al. (2009) have defined cloud computing as a distributed system consisting of a constellation of interconnected, virtualised computers. ...
This paper examines how cloud services impact IT providers' service profiles and how employees' skills necessarily change as a result. In our study, we assessed market changes for small and medium-sized enterprises as potential threats and examined future services expected from IT service providers (e.g., adaptations and innovation) as intervention measures. To identify and describe vulnerabilities within that context, we surveyed 36 experts at six German and Austrian IT service providers using a questionnaire based on a literature review that yielded a brief outline of the history of cloud computing. When the processes of organising and interpreting significant ratings were analysed in hierarchical cluster analysis, results highlighted the need for IT service providers to adjust to the demands of cloud computing, which prompts a shift in employees' skill sets from predominantly technical skills to predominantly business-oriented ones.
... References to cloud computing in its modern sense appeared early as 1996, with the earliest known mention in a Compaq internal document. [3] The popularization of the term can be traced to 2006 when Amazon.com introduced the Elastic Compute Cloud. The underlying concept of cloud computing dates to the 1950s, when large-scale mainframe computers were seen as the future of computing, and became available in academia and corporations, accessible via thin clients/terminal computers, often referred to as "static terminals", because they were used for communications but had no internal processing capacities. ...
... • Device and location independence enable users to access systems using a web browser regardless of their location or what device they use (e.g., PC, mobile phone). [3] As infrastructure is off-site (typically provided by a third-party) and accessed via the Internet, users can connect from anywhere. • Maintenance of cloud computing applications is easier, because they do not need to be installed on each user's computer and can be accessed from different places. ...
... In the SaaS model, cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients. [3] To accommodate a large number of cloud users, cloud applications can be multitenant, that is, any machine serves more than one cloud user organization. ...
... The use of this tool opens up possibilities for efficient analysis and processing of large volumes of information, even when it is critical, overcoming this challenge in modern scientific work. Its capabilities enable researchers to 50 Vouk, M. A. (2008 ...
... Google Apps Script дозволяє створювати автоматизовані резервні копії та забезпечує захист даних, надаючи можливість відновлення інформації в разі їх втрати. Додатково, інтеграція з іншими інструментами Google, такими як Google Forms або Google Analytics, дає можливість автоматично збирати дані та аналізувати їх безпосередньо у Google Sheets, забезпечуючи єдність процесу обробки і аналізу (Shiohara, 2014;Vouk, 2008). ...
The article focuses on the role and potential of using the Google Apps Script platform as a tool for automating data analysis in scientific research for future specialists in the educational sphere. Thus, the article aims to systematize and summarize information about the possibilities and advantages of using digital tools, particularly the Google Apps Script platform, for automating data analysis in scientific research. The following research methods were employed: analysis and summarization of technological capabilities and practical cases using the Google Apps Script platform based on existing scientific and methodological sources, modelling, and synthesis of obtained data. This contributed to understanding and assessing the potential of the designated tool for use by future specialists in the educational sphere. The possibility of optimizing various stages of scientific research using Google Apps Script was thoroughly analysed, particularly in data filtering and summarization, statistical analysis, creating graphs, and reporting. Considering the prospects of applying the synthetic method in the research process, the role of the specified tool in creating and expanding data for objective analysis is emphasized. The Google Apps Script platform facilitates the effective combination of analytical capabilities and automation, allowing researchers to better synthesize the obtained results and react promptly to the identified trends. Such a comprehensive approach to data processing creates unique opportunities for innovative research in the field of education, making it more accessible and interactive for use by researchers and practitioners. As a result of the study, the significance of digital tools, including the Google Apps Script platform, in preparing future education professionals was highlighted. The advantages and possibilities of this tool for implementing innovative approaches in data analysis in the field of scientific research were pointed out, emphasizing its potential for developing new methodologies and approaches in this field. In the context of the on-going evolution of educational approaches, the Google Apps Script platform serves not only as a data analysis tool but also as a catalyst for innovative thinking. In summary, it is worth noting that Google Apps Script is a powerful tool that provides opportunities for automating complex data processing processes in scientific research, even when employing the synthetic method. The use of this tool opens up possibilities for efficient analysis and processing of large volumes of information, even when it is critical, overcoming this challenge in modern scientific work. Its capabilities enable researchers to focus on essential aspects of research, reducing time spent on routine data processing operations.
... Cloud computing merupakan sebuah evolusi dari teknologi informasi yang menyediakan layanan dan produk sesuai dengan permintaan pengguna. [5]Pengembangan ideide yang baru dan inovatif untuk sebuah layanan internet yang baru tidak lagi membutuhkan modal yang besar pada layanan tersebut maupun biaya sumber daya manusia yang mahal untuk mengoperasikannya. [6] Penelitian ini bertujuan menganalisa pemanfaatan cloud computing dalam pengembangan bisnis mulai dari cara mengadopsi cloud sesuai dengan kebutuhan organisasi bisnis. ...
Selama beberapa tahun terakhir, Internet telah berkembang sangat pesat dalam jangkauan yang luas. Internet telah tersedia dan dapat diakses oleh semua orang. Namun, masalah lain seperti ukuran penyimpanan, daya yang dikonsumsi oleh peralatan dan biaya perangkat keras terus meningkat. Ruang penyimpanan di pusat data tidak lagi memenuhi permintaan yang semakin meningkat. Inovasi komputasi awan telah muncul dalam upaya untuk memecahkan ini dan masalah lingkungan lainnya. Ini telah mengubah fase industri teknologi informasi, menjadikan perangkat lunak lebih menarik sebagai layanan dan membentuk cara perangkat keras teknologi informasi dirancang dan dibeli. Teknologi informasiberkembang dan semakin maju.Komputasi awan telah menjadi populer di industri TI. Ini adalah server virtual yang tersedia melalui Internet; yang memungkinkan pengguna untuk mengakses sumber daya dan layanan komputasi, terlepas dari waktu dantempat. Penyedia komputasi awan terkenal termasuk Amazon Web Services (AWS), Microsoft Windows Azure, dan Google AppEngine. Komputasi awan tetap penuh dengan resiko. Keamanan, kerahasiaan, kemampuan audit, kepatuhan terhadap peraturan dan sejumlah resiko lainnya harus diperiksa dengan cermat sebelum keterlibatan apa pun di bidang ini.
... Such an exercise would require systematically executing a MEM for potentially hundreds of 127 retrospective forecasts, and analysing large volumes of spatial-temporal model output. This 128 would require computing power far beyond a single workstation, and although the concept of 129 using the combined power of a network of computers to solve demanding computational tasks 130 dates at least back to the 1970's (e.g., Farber, 1970;Jones & Schwans, 1979;Vouk, 2008 ), the 131 MEM community is mostly unable to utilise distributed computing power due to compounding 132 challenges. Inherent limitations related to their computation complexity and structure, with long 133 iterative run times to represent non-linear processes at different temporal and spatial scales that 134 cascade through food webs make MEMs incompatible with common high-performance 135 computing technologies and computing scientific software execution infrastructures ( is a complex field that combines understanding of marine biology and ecology, biochemistry, 159 hydrology, fisheries dynamics and socio-economics, and that relies on the operation of a wide 160 range of complex software tools to process, generate and analyse data. ...
Marine Ecosystem Models (MEMs) are increasingly forced with Earth System Models (ESMs) to better understand marine ecosystem dynamics, and to analyse the effects of alternative management efforts for marine ecosystems under potential scenarios of global change. However, policy and commercial activities typically occur on seasonal-to-decadal time scales, a time span widely used in the global climate modelling community but where the skill level assessments of MEMs are in their infancy. This is mostly due to technical hurdles that prevent the global MEM community from performing large ensemble simulations with which to undergo systematic skill assessments. Here, we developed a novel distributed execution framework constructed of low-tech and freely available technologies to enable the systematic execution and analysis of linked ESM / MEM prediction ensembles. We apply this framework on the seasonal-to-decadal time scale, and assess how retrospective forecast uncertainty in an ensemble of initialised decadal Earth System Model predictions affects a mechanistic and spatiotemporal explicit global MEM. Our results indicate that ESM internal variability has a relatively low impact on the MEM predictability in comparison to the broad assumptions related to reconstructed fisheries. We also observe that the results are also sensitive to the ESM specificities. Our case study warrants further systematic explorations to disentangle the impacts of climate change, fisheries scenarios, MEM internal ecological hypotheses, and ESM variability. Most importantly, our case study demonstrates that a simple and free distributed execution framework has the potential to empower any modelling group with the fundamental capabilities to operationalize marine ecosystem modelling.
... However, a multidimensional prediction of resources is a challenging task, especially when predicting multiple resources at once by the same predictor as proposed in this work. This implies to handle with different consumption behaviour of the different resource type which may vary highly for a resource and not for another [17] and thus, to learn different consumption behaviour by the same learner. Several works, methods, and techniques have been proposed for resource utilization prediction in virtualized networks. ...
With recent developments in cloud computing, massive unplanned traffic loads are submitted to cloud platforms. High traffic load variations lead to uncertainty in resource utilization. Therefore, efficient data-driven mechanisms for automatic resource management become crucial. These mechanisms enable complex and distributed systems to anticipate and efficiently react to workload fluctuations. They rely on accurate resource utilization prediction techniques to satisfy the resource needs, in order to fulfill the service level objective for cloud applications and infrastructures. In this paper, we propose a deep learning model to predict the resource consumption (e.g., CPU, memory) in network function virtualization infrastructures. We model an augmented graphical neural network (GNN) that exploit neighbouring relationships between virtual network functions (VNF) composing various service function chains (SFC), and use an augmented feature vector allowing to capture the consumption evolution of a VNF. The model enables to predict the resource needs of VNFs by identifying the multidimensional dependencies according to the graph structure of an SFC. The proposed GNN model has been compared with MLP, LSTM, hybrid LSTM and CNN models to evaluate its accuracy and efficiency. Real word datasets have been used to evaluate the proposed model using five performance metrics. The performance analysis reveals that our graph-features based GNN model outperforms the other models for SFCs with high traffic load variation.
... Networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" (Mell and Grance, 2011, p. 2). Cloud enables service-based technology provisioning (Etro, 2009;Vouk, 2008), allowing organizations to move from asset ownership to the acquisition of computing as-aservice (Van der Molen, 2009;Vouk, 2008). The on-demand, minimum management effort, limited provider interaction and shift of asset ownership characteristics of cloud have shaped a perception of cloud adoption as a binary and one-off technology adoption decision. ...
... Networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction" (Mell and Grance, 2011, p. 2). Cloud enables service-based technology provisioning (Etro, 2009;Vouk, 2008), allowing organizations to move from asset ownership to the acquisition of computing as-aservice (Van der Molen, 2009;Vouk, 2008). The on-demand, minimum management effort, limited provider interaction and shift of asset ownership characteristics of cloud have shaped a perception of cloud adoption as a binary and one-off technology adoption decision. ...
Purpose
The authors study how cloud adoption decision making unfolds in organizations and present the dynamic process leading to a decision to adopt or reject cloud computing. The authors thus complement earlier literature on factors that influence cloud adoption.
Design/methodology/approach
The authors adopt an interpretive epistemology to understand the process of cloud adoption decision making. Following an empirical investigation drawing on interviews with senior managers who led the cloud adoption decision making in organizations from across Europe. The authors outline a framework that shows how cloud adoptions follow multiple cycles in three broad phases.
Findings
The study findings demonstrate that cloud adoption decision making is a recursive process of learning about cloud through three broad phases: building perception about cloud possibilities, contextualizing cloud possibilities in terms of current computing resources and exposing the cloud proposition to others involved in making the decision. Building on these findings, the authors construct a framework of this process which can inform practitioners in making decisions on cloud adoption.
Originality/value
This work contributes to authors understanding of how cloud adoption decisions unfold and provides a framework for cloud adoption decisions that has theoretical and practical value. The study further demonstrates the role of the decision-leader, typically the CIO, in this process and identifies how other internal and external stakeholders are involved. It sheds light on the relevance of the phases of the cloud adoption decision-making process to different cloud adoption factors identified in the extant literature.
... Importantly, the ecosystem is one of the crucial components of smart cities and their extended smart organizations, enhancing information exchange and knowledge management between organizations and institutions, and it can be formed by the fundamental cloud technique (Abdollahi et al., 2023;Del Chiappa & Baggio, 2015;Harrison et al., 2010). Without the cloud, representing the internet, it becomes challenging to achieve smart hospitality (Sultan, 2010;Vouk, 2008). Particularly from a customer perspective, digital experiences have become a crucial component in the customer journey, and digital platforms have already had a strong impact on their experiences. ...
... Among the existing and evolving ICTs, cloud computing has dramatically altered business operations. It serves as a bridge that connects the physical and digital worlds to enable smartness (Manogaran et al., 2021) and also offers services that are accessible ubiquitously (Sultan, 2010;Vouk, 2008). The American National Institute of Standards and Technology (NIST) defines cloud computing as a model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (Mell & Grance, 2011, p. 2). ...
This study aims to explore the role of cloud computing in the creation of smart hospitality experiences and bridge the existing gap in knowledge. The research adopts a mixed research approach, combining qualitative and quantitative methods, to investigate and prioritize criteria that contribute to smart hospitality through cloud computing. Expert perspectives are utilized to develop a comprehensive two-dimensional cloud-based smart hospitality experience model with fourteen subdimensions, highlighting the pivotal role of cloud computing in facilitating smart hospitality. The study uncovers the interrelations and relative weights among the criteria, shedding light on the fundamental significance of cloud computing in creating smart hospitality experiences. This study focuses on the untapped potential of cloud computing in the context of smart hospitality. It contributes to the existing body of knowledge by providing theoretical insights for interdisciplinary hospitality operations and practical guidance for hospitality practitioners to achieve sustainable outcomes.
... Furthermore, users can test candidate services before adopting (Surya et al., 2014) quickly and without a large investment. Organizations can also move their technology expenditures from capital expenditure (CapEx) to operational expenditure (OpΕx) (Van der Molen, 2009;Vouk, 2008) enabling more flexibility in temporally adjusting their expenditure. Cloud services thus offer strategic flexibility (Benlian et al., 2009) since organizations can extend or eliminate services on-demand. ...
... Adoption was thus emergent. This characteristic provides flexibility in terms of cost, as organizations can also adjust their technology expenditure across time (Van der Molen, 2009;Vouk, 2008) and even partially adopt a service, thus enabling organizations to minimize risks associated with technology decisions and to grow use incrementally. ...
We show that proximity is significant during cloud computing’s adoption. This is counter to the prevailing assumptions of cloud adoption as being more impersonal and distant, with less interaction between provider and purchaser than on-premises. We do this through an interpretive study of cloud computing adopters across Europe. We develop a conceptual framework of cloud proximity which draws attention to its locational, relational and temporal proximal dimensions. Our proximal analysis leads us to identify three aspects of cloud adoption where proximity plays a key role: mercantile aspect (e.g., cloud sales support), counsel aspect (e.g., access to internal and external expertise) and organi-technical aspect (e.g., the understanding of cloud technology and services alongside their organizational adoption context). By challenging assumptions of distant and remote adoption, we contribute to the cloud computing adoption research and raise questions for IT adoption in general.
