On-premise Systems Research Articles

Assessing the transformative impact of cloud computing on software deployment and management

Cloud computing has fundamentally transformed the landscape of software deployment and management, offering significant benefits and reshaping traditional approaches. This review explores the transformative impact of cloud computing on these domains, highlighting key changes and advantages. Firstly, cloud computing has revolutionized software deployment by introducing scalable and flexible infrastructure solutions. Unlike traditional onpremises systems that require significant upfront investment and ongoing maintenance, cloud platforms offer ondemand resources and payasyougo models. This shift enables organizations to deploy software rapidly, adapt to changing needs, and scale resources efficiently without the constraints of physical hardware. Additionally, cloud computing enhances software management through centralized control and automation. Cloud environments provide integrated management tools that streamline the deployment, monitoring, and maintenance of applications. These tools facilitate automated updates, patch management, and system backups, reducing the burden on IT teams and minimizing downtime. Furthermore, cloudbased management systems offer realtime visibility and analytics, allowing for proactive performance monitoring and troubleshooting. The collaborative nature of cloud computing also fosters improved development and deployment practices. Cloud platforms support DevOps methodologies by enabling continuous integration and continuous delivery (CI/CD) pipelines. This integration accelerates software development cycles, enhances collaboration among distributed teams, and ensures consistent and reliable deployments. Moreover, the cloud's global reach and accessibility break down geographical barriers, allowing organizations to deploy software across multiple regions effortlessly. This geographic flexibility enhances the user experience and ensures high availability and performance, regardless of the user's location. Despite these advancements, the transition to cloud computing presents challenges, including data security and compliance concerns. Organizations must implement robust security measures and adhere to regulatory requirements to protect sensitive information and maintain trust. In conclusion, cloud computing has had a profound impact on software deployment and management, offering scalable, flexible, and efficient solutions. Its transformative effects include streamlined operations, improved collaboration, and global accessibility. As cloud technology continues to evolve, organizations must navigate associated challenges while leveraging its benefits to drive innovation and efficiency in software management. Keywords: Management, Impact, Cloud Computing, Software Deployment, Assessing

Exploring evolving H.R. and recruitment strategies in the age of technology advancements based on artificial intelligence

With the advancement of Technology, it has become imperative for H.R. and recruitment strategies to adapt. This paper will highlight the emerging trends in H.R. and Recruitment driven by Technology. One prominent trend in H.R. Technology is the increasing prevalence of artificial intelligence (A.I.). A.I. is already utilized to streamline and automate H.R. processes like resume screening, interview scheduling, and performance reviews. Looking ahead, A.I. is anticipating a significant impact on H.R. by aiding organizations in making better hiring decisions, designing personalized training programs, and fostering more engaging employee experiences. Another notable trend in H.R. Technology revolves around the transition towards cloud-based platforms. These platforms offer advantages over on-premises systems, including scalability, flexibility, and cost-effectiveness. As a result of the various benefits brought by Technology, we would bring about the critical themes in the form of the strategies that the organizations should adopt to enhance their productivity in Recruitment and selection with the help of bibliometric analysis, thematic analysis, ISM (interpretive structuring model) and MICMAC analysis.

Development of an intelligent system for doctor-patient interaction in a day hospital

The article discusses the issue of finding optimal digital solutions to streamline organizational processes for medical organizations when providing medical care in a day hospital setting. Based on the analysis of in-depth interviews with medical specialists, the specific features of the system of providing medical care in a day hospital setting are described, and areas for optimizing the procedure for shortterm hospitalization at the present stage are identified. Taking into account the needs of practical healthcare in digital technologies, an algorithm for the functioning of a novel intellectual product (decision support system based on the intellectual processing method) is presented, including an onpremise system for monitoring, diagnosis, and analysis of medical data (condition self-assessment, satisfaction with aspects of treatment, etc.).

From on-premise to cloud: Evolving IT infrastructure for the AI age

The transition from traditional on-premise IT infrastructure to cloud-based systems represents a fundamental shift necessary for the advancement and implementation of artificial intelligence (AI) technologies. This paper explores the critical role of cloud computing in supporting AI, emphasizing its benefits in terms of scalability, flexibility, and cost-efficiency. It examines the limitations of on-premise systems, the advantages of various cloud models (IaaS, PaaS, SaaS), and key considerations for successful cloud migration, such as data security, compliance, and cost analysis. Through a detailed case study of a financial institution's cloud migration for AI implementation, this study highlights the challenges, solutions, and lessons learned in the process. The case study provides practical insights into the phased approach to cloud migration, the use of hybrid cloud solutions as transitional strategies, and the importance of training and skill development for IT teams to ensure business continuity during the transition. Furthermore, the paper discusses future trends in cloud and AI integration, including the impact of emerging cloud technologies like server less and edge computing, and the pivotal role of 5G in enhancing cloud-based AI applications. It predicts the future landscape of cloud and AI integration, emphasizing the increasing adoption of hybrid and multi-cloud strategies, investment in data privacy and security, and the integration of AI with other emerging technologies such as IoT and block-chain. The paper concludes by underscoring the importance of cloud infrastructure in the AI age and calls for businesses to evaluate their cloud readiness to stay competitive in a rapidly evolving digital landscape. By embracing cloud-based solutions, businesses can unlock new opportunities for innovation, efficiency, and growth, positioning themselves at the forefront of the AI revolution.

Cloud-based Data Analytics for Business Intelligence

Abstract: In the era of data-driven decision-making, organizations are turning to cloud-based data analytics for business intelligence to overcome the limitations of traditional on-premises systems. This paradigm shift offers the promise of scalable, agile, and advanced analytics capabilities. This paper explores the landscape of cloud- based data analytics for business intelligence by investigating existing systems, challenges, and opportunities. The study first examines leading cloud platforms such as Amazon Web Services (AWS) Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and Snowflake[11]. It evaluates their features, scalability, and integration options to assess their suitability for modern BI needs. Moreover, the research identifies critical challenges in transitioning to cloud-based analytics, including data integration complexities, security concerns, and cost management. The integration of advanced analytics techniques, such as machine learning and AI, into cloud- based environments is also explored. The study delves into the benefits and challenges of predictive analytics, anomaly detection, and other emerging capabilities that empower organizations to extract deeper insights from data. Furthermore, hybrid cloud architectures, which combine on-premises infrastructure with cloud resources, are investigated. Strategies for seamless data integration and workload distribution are discussed, enabling organizations to strike a balance between performance and data governance.

Efficient end-to-end long-read sequence mapping using minimap2-fpga integrated with hardware accelerated chaining

minimap2 is the gold-standard software for reference-based sequence mapping in third-generation long-read sequencing. While minimap2 is relatively fast, further speedup is desirable, especially when processing a multitude of large datasets. In this work, we present minimap2-fpga, a hardware-accelerated version of minimap2 that speeds up the mapping process by integrating an FPGA kernel optimised for chaining. Integrating the FPGA kernel into minimap2 posed significant challenges that we solved by accurately predicting the processing time on hardware while considering data transfer overheads, mitigating hardware scheduling overheads in a multi-threaded environment, and optimizing memory management for processing large realistic datasets. We demonstrate speed-ups in end-to-end run-time for data from both Oxford Nanopore Technologies (ONT) and Pacific Biosciences (PacBio). minimap2-fpga is up to 79% and 53% faster than minimap2 for sim 30times ONT and sim 50times PacBio datasets respectively, when mapping without base-level alignment. When mapping with base-level alignment, minimap2-fpga is up to 62% and 10% faster than minimap2 for sim 30times ONT and sim 50times PacBio datasets respectively. The accuracy is near-identical to that of original minimap2 for both ONT and PacBio data, when mapping both with and without base-level alignment. minimap2-fpga is supported on Intel FPGA-based systems (evaluations performed on an on-premise system) and Xilinx FPGA-based systems (evaluations performed on a cloud system). We also provide a well-documented library for the FPGA-accelerated chaining kernel to be used by future researchers developing sequence alignment software with limited hardware background.

Integrating Cloud-Native Solutions in Financial Services for Enhanced Operational Efficiency

In an industry marked by fast technological advancement and strict regulatory demands, the incorporation of cloud-native solutions in financial services has become a crucial approach for improving operational efficiency. Cloud-native architectures, which are specifically designed to use the complete functionalities of cloud computing environments, provide substantial benefits compared to conventional on-premises systems. This study investigates the ways in which financial institutions might use these benefits to attain enhanced agility, scalability, and cost-efficiency. Cloud-native solutions are constructed by using microservices, containerisation, and continuous integration/continuous deployment (CI/CD) methodologies, offering organisations the ability to build applications with greater speed and efficiency. Through the use of these technologies, financial services companies may enhance their capacity to adapt to market fluctuations, optimise their information technology infrastructure, and refine their operations. Microservices enable the creation of modular application aarchitecture, which simplifies adjustments and maintenance processes while minimising periods of system unavailability. Containerisation, using infrastructure such as Docker and Kubernetes, improves the efficient use of resources and ensures consistent operation in various settings. Continuous Integration/Continuous Deployment pipelines optimise development cycles by facilitating regular upgrades and expediting the release of new features.

Machine Learning Algorithms for Cloud Computing Security: A Review

: Cloud computing has revolutionized the way businesses and individuals access and manage IT resources. However, cloud computing also introduces new security challenges. Cloud-based systems are often more complex and distributed than traditional on-premises systems, making them more vulnerable to attack.Machine learning (ML) is a powerful tool that can be used to analyze cloud computing attacks and improve cloud security. ML algorithms can be trained on large datasets of attack data to learn the patterns and characteristics of different types of attacks. Once trained, ML models can be used to detect new attacks in real time and recommend mitigation strategies. This paper reviews the state of the art in using ML to analyze cloud computing attacks. It discusses the different ways that ML can be used to improve cloud security, including developing intrusion detection systems (IDS), anomaly detection systems, and attack forecasting systems. The paper also discusses the challenges of using ML for cloud security, such as the need for large and high-quality training data and the need to adapt ML models to new and emerging threats.The paper concludes by discussing the future of using ML for cloud security. It argues that ML is a key technology for improving cloud security in the face of increasingly sophisticated and targeted attacks.

Investigating Cloud ERP Adoption in Australian Higher Education Institutions: A Case Study

Background: Some higher education institutions (HEIs) are constantly under pressure to deliver superior quality education services at low costs through deploying traditional on-premise systems. Cloud-based enterprise resource planning (Cloud ERP) presents an ideal opportunity to lower HEI costs in terms of scalability and pay-per-use features. Adoption of Cloud ERP is, however, still low for the context of HEIs. The successful adoption of Cloud ERP depends not only on the support of system providers but also on understanding Cloud ERP adoption from the client organization perspective. This study explores Cloud ERP adoption in the context of Australian HEIs. Method: This study adopted a case study methodology involving the in-depth semi structured interviews of several key stakeholders. Thematic analysis was used to analyze and interpret interview data. Results: Based on the case study, our findings suggest that this particular Australian HEI was subject to strategic, operational, technological, and financial motives originating from either internal or external locus. Most of the expected motives were realized, except the full flexibility of Cloud ERP. Four major challenges of Cloud ERP implementation were found. Conclusion: This study empirically investigates Cloud ERP adoption in HEIs by identifying the motives, realized benefits and challenges of Cloud ERP adoption, which bridges the research gap of this topic. This study could assist Cloud ERP providers to adjust their marketing strategies to promote the adoption rate of Cloud ERP in HEIs. Understanding the motives, challenges and benefits of IT adoption in HEIs facilitates judicious decision-making prior to selection and minimizes the possibility of the failure of Cloud ERP adoption.

Public Cloud to Cloud Repatriation Trend

Cloud repatriation is the process of moving a workload or application from a public cloud to an on-premises or private cloud. The statistics of cloud repatriation, its possible effects, and the justifications for company adoption are all included in the paper. According to recent estimates, in 2021, over 80% of US businesses will pull some workloads from the public cloud. The statistic of the Public Cloud to Cloud Repatriation Trend initially seems to indicate a significant return to on-premises systems.

Challenges of Automatic Speech Recognition for medical interviews - research for Polish language

Automatic Speech Recognition (ASR) systems are quickly becoming a crucial element in supporting healthcare providers, improving the flow of information among medical teams, and enhancing the patient's experience. However, to be fully supportive, these ASR systems must meet certain requirements dictated by market realities: high accuracy of speech recognition and low rate of errors, the possibility of additional training the model, and the possibility of on-premise system installation. Therefore, the aim of this paper is to perform a comparative analysis of leading ASR systems available on the Polish market for the needs of conducting medical interviews. We selected three systems, Google ASR, Microsoft ASR, and Techmo ASR, and we compared their performance on a prepared data set of medical-related expressions spoken in Polish. The results of our analysis indicated that there are minor discrepancies in the accuracy of speech recognition between all three evaluated ASR systems, whereas only two ASR systems met the raised requirements, in both cases partially. Still, they all exhibited specific problems in recognising word endings or word boundaries. We were able to categorise such problems into: Misrecognitions, Quality Problems, and Word Boundaries, varying in their level of influence on the further speech recognition process. Our research findings are expected to provide valuable insights to a wide range of stakeholders facilitating the development of tailored speech recognition solutions that meet the specific needs of medical sector.

Cloud infrastructure architecture and the zero trust model as a cybersecurity strategy

Since the emergence of the internet, the vertiginous growth in the use of electronic media has grown in the same proportion as cyber crimes, applied in an increasingly sophisticated way. In addition to these two factors that impact the use of the internet, the speed with which the resources and tools inherent in the area of Information and Communication Technology (ICTs) evolve, which enhance the need to continuously develop and improve the means for the protection of sensitive data of people and public and private organizations. In this perspective, Cloud Computing emerges, which allows the storage of data, networks and applications, and other resources through integrated environments through the internet, from collective providers, as opposed to the on-premise system, which is based on the custody and access through local servers, including mainframes, which are still maintained in most large organizations, such as the banking system, for example. Added to Cloud Computing are Zero Trust practices, whose main innovation is the adoption of several layers of access verification. This article was prepared using bibliographical research as a methodology. The question that arises on the subject is: how does Zero Trust provide greater security to network users? The objective is to demonstrate the advantages of security provided by Zero Trust, combined with Cloud Computing. In view of the analyzed literature, it was possible to conclude the existence of two main aspects brought by Zero Trust: internet user access only from 7 layers of verification, and the mitigation of vulnerabilities, including the idea of responsible browsing by different users.

Analysis of Personalized Recommendation System on Cloud Platform

A good recommendation system can recommend a group of movies to users based on their interests or the popularity of the films. The recommendation systems are important because they assist them in making good choices without requiring them to expend their time. It is difficult and expensive for programmers to build an on-premise recommendation system to automate this process as there is a massive increase in data volume which requires high computational capacity. This paper describes the deployment of a movie recommendation engine in a cloud storage environment that uses ALS algorithm and POST API. The advantage of such a deployment is the use of cloud factors in the generation of recommendations, the cloud and the cloud environment promises high availability and thus reduces downtime for recommendation services.

Cloud Computing Transformation Considering Operational Efficiency

Companies that have their own IT Center built and operated an on-premise system. But recently, cloud computing is considered to secure system reliability and reduce management costs. IT resources can be efficiently utilized and paid for used. Therefore, companies were deploying and migrating their intranet services to cloud servers. However, it is also true that cloud migration makes system complexity increase, and system reliability is compromised by the increase of management points. Cloud computing for each service unit was considered as a way to meet an efficient utilization of IT resources without compromising existing system reliability when providing new services. Cloud system enables real-time orchestration, and has excellent security. As a hybrid method that implemented cloud computing while maintaining the existing infrastructure, it was possible to operate an uninterrupted system as a result of system construction, and security was also enhanced. Cloud computing used to as a service unit, that both operational efficiency and functionality can be satisfied.

Cloud Adoption in Accounting Information Systems in Asia & SOC 2® Report – An Empirical Study on Industry’s Perspective

ABSTRACT Many business and technology organizations see cloud adoption and migrating existing systems to the cloud as an accelerator of digital transformation. The benefits of cloud adoption are perceived as increased scalability and cost reduction. At the same time, there are concerns about whether the information on cloud-based systems is secure and whether the privacy of the data in a cloud environment is at risk. This research brings out the industry’s perspective, both from an end-user perspective as well as IT transformation and IT procurement decision makers of accounting information systems and enterprise resource planning systems in Asia geography, on the preference for cloud-based or on-premise systems, top enablers for cloud adoption and importance of SOC 2® as an assurance for information security and data privacy concerns. The findings of this research indicate that decision-makers for IT transformation and IT procurement prefer cloud-based accounting information systems and enterprise resource planning systems over on-premise systems. Scalability, cost reduction, business agility, business continuity and disaster recovery, and enhanced collaboration are top enablers for cloud adoption. This research also indicates that the SOC 2® report is increasingly seen to address information security and data privacy concerns. Keywords: SOC 2®; Trust services criteria; Information security; Data privacy; Cloud computing; Accounting Information Systems

Migrating a research data warehouse to a public cloud: challenges and opportunities.

ObjectiveClinical research data warehouses (RDWs) linked to genomic pipelines and open data archives are being created to support innovative, complex data-driven discoveries. The computing and storage needs of these research environments may quickly exceed the capacity of on-premises systems. New RDWs are migrating to cloud platforms for the scalability and flexibility needed to meet these challenges. We describe our experience in migrating a multi-institutional RDW to a public cloud.Materials and MethodsThis study is descriptive. Primary materials included internal and public presentations before and after the transition, analysis documents, and actual billing records. Findings were aggregated into topical categories.ResultsEight categories of migration issues were identified. Unanticipated challenges included legacy system limitations; network, computing, and storage architectures that realize performance and cost benefits in the face of hyper-innovation, complex security reviews and approvals, and limited cloud consulting expertise.DiscussionCloud architectures enable previously unavailable capabilities, but numerous pitfalls can impede realizing the full benefits of a cloud environment. Rapid changes in cloud capabilities can quickly obsolete existing architectures and associated institutional policies. Touchpoints with on-premise networks and systems can add unforeseen complexity. Governance, resource management, and cost oversight are critical to allow rapid innovation while minimizing wasted resources and unnecessary costs.ConclusionsMigrating our RDW to the cloud has enabled capabilities and innovations that would not have been possible with an on-premises environment. Notwithstanding the challenges of managing cloud resources, the resulting RDW capabilities have been highly positive to our institution, research community, and partners.

Analysis of Cloud Computing Security Issues and Risks

In the world of computer networking, cloud computing makes a technical shift of computing services being provided locally to being provided remotely by third party service providers. The data which was previously kept under control of users now under the control of service providers cloud computing brings many financial and functional benefits as well as serious security concerns that may threaten business continuity and corporate reputation. The definition of cloud computing is still blurry in a large part, because of the magnitude of the security risks and the virtually unlimited amount of information being published over the unsecure platform. The purpose of this paper is to assess how security risk factors are affecting the existing and prospective cloud users’ cloud usage strategies. Are they simply betting that financial benefits will surpass security risks, or are they confident that cloud providers are capable of assuring an equal or higher level of security than on-premise systems? This survey, through the examination of published materials and studies, analyzes existing issues along with available countermeasures in order to evaluate the overall assurance level of cloud security. The primary goal of the survey is to assess how security concerns have affected or will affect the respondents’ decision on adopting cloud and its services. This study includes basics of cloud computing by adding its characteristics, models and their categories. Analysis also embraced the existing security concerns faced by researchers and their imposed methodologies.

Enterprise Applications Reimagined-Cloud abridges mystery of Mobile Offline Working

Abstract Software as a Service (SaaS) has the potential to transform the way information-technology departments relate to and even think about their role as providers of computing services to the rest of the enterprise. In this paper we present an empirical case study on how cloud computing eases the development of mobile applications that work efficiently even with intermittent network access. We refer to a situation as offline where there is limited or no internet connectivity. We draw comparisons between the rudimentary way of creating offline mobile applications (via On-premise mobile platforms) to the new cloud development model. With the help of measurable KPIs (Key Performance Indicators), we conclude the effectiveness of development on Cloud. Additionally, we have taken a case study instance of enterprise mobile application that is used for data capture at remote locations in Oil fields. Then we describe design implementation details as well as provide recommendations for seamless integration to backend On-premise (OP) systems.

Determinants of cloud ERP adoption in Jordan: an exploratory study

Enterprise resource planning (ERP) systems are usually adopted through two major options: cloud computing or on-premise infrastructure. The issues related to hardware, servers, implementation costs, and facilities, which are necessary to run on-premise ERP systems, are rather great for small and mid-sized enterprises (SMEs). The purpose of this research is to highlight the factors influencing cloud ERP adoption among SMEs. The technological, organisational, and environmental (TOE) model will be used as a theoretical base. This qualitative, exploratory study gathers data from 13 different SMEs and cloud ERP service providers through semi-structured interviews, focusing on SMEs in Jordan. According to our results, support from both service providers and top management within the company play an important role in whether a firm adopts cloud ERP services, in addition to a number of other factors at varying rates. Research findings can be used by service providers and business owners to enhance their approach to cloud ERP by showing the reasons why some SMEs chose to adopt this technology and others did not. Cloud ERP providers need to intensify their efforts to build a progressive environment for their services that will eliminate any ambiguity regarding this type of technology.

Optimized cloud-based scheduling for protein secondary structure analysis

In the domain of proteomics, an in-depth analysis of the 3D structure of a protein is of paramount importance for many biological studies and applications. At the secondary level, protein structure can be described in terms of motifs, recurrent patterns of smaller biological structures called secondary structure elements. In this paper, the focus is on the identification of geometrical motifs in different proteins using the Cross Motif Search (CMS) algorithm. Such task, due to the high computational cost of CMS with respect to traditional alignment algorithms, is very demanding, and thus, parallel processing is mandatory. In previous papers, CMS parallelization has been already studied from the HPC standpoint. Since cloud computing is emerging as an alternative to on-premise HPC systems, it is worthwhile examining the feasibility and possible advantages in terms of both performance and costs, of migrating to a cloud implementation. This paper is an extension of a preliminary work carried out on the cloud parallelization of CMS. The paper has two main contributions. First of all, an analytic model of the communication pattern of CMS is described, in order to get insights on the performance of the application when executed on a cloud infrastructure. Secondly, an optimized “location-aware” scheduling policy to assign workload to the application workers is introduced, in order to minimize internode communication in a cloud setting. Experiments are presented in order to validate the newly introduced scheduling policy and assess the performance of the cloud implementation of CMS. The results presented in this paper are general, in the sense that they can be applied to any other algorithm with a communication pattern similar to the one of the target applications.