Cloud systems are the internet-based delivery of various services, including information storage, workstations, spreadsheets, connectivity, and application programs. Subramanian and Jeyaraj (2018) describe cloud computing as a model for offering appropriate and on-demand web accessibility to a shared pool of configurable circuits, memory, data centers, technology, and services that can be instantly liberated with little contact and oversight from the vendor. On-demand self-service, high-performance network access, rapid elasticity, high scalability, and measured offering are the features of the operating category (Abdel-Basset et al., 2018). In addition, it illustrates four deployment strategies, including blended, communal, personal, and commercialized clouds. Public cloud firms charge for their online-based services. On the other hand, private cloud solutions are limited to a specific number of visitors.
These offerings are a network-based framework that offers hosted operations. Therefore, this is combined with the three service paradigms, PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) (Abdel-Basset et al., 2018). Subramanian and Jeyaraj’s (2018) description of cloud computing includes the necessary architecture and fundamental traits, including Virtualization, Uniformity, Regional Deployment, and Responsiveness. Cloud service suppliers allow consumers to store documents and apps on remote systems and then retrieve the information through the internet. Thus, this allows the user to operate anywhere since the client is not obliged to be in a certain location to view it. Moreover, it transfers all of this labor to multiple computers in different cyberspaces (Abdel-Basset et al., 2018). As a result, the internet becomes the storage, and presto, a person’s information, activity, and apps are accessible from any Internet-connected equipment, anywhere around the globe.
Literature Review
As communication science progresses fast, cloud services have taken on a more prospective position in IT by providing customers with storage. Cloud systems have allowed companies to lease their solutions at an hourly cost. Therefore, this literature study contains the present status of cloud system research from an organizational standpoint. Diverse researchers have described fundamental cloud system paradigms, including SaaS, PaaS, IaaS, and CaaS. From the standpoint of cloud security suppliers and users, studies have claimed that these frameworks play a significant role in data protection. Alouffi et al. (2021) listed cloud resources as well-known ways of providing information exchange and sharing offerings for tracking the inventory of production plants. Therefore, fraudulent online services prohibit industrial players from correctly acquiring merchandise identification.
As such, cloud system technologies are the dominant contributor to these issues and risks. An additional recent study by Merz et al. (2020) suggests Acics as a uniform and rapid inspection framework for huge amounts of industrial data in an unsecured virtualization setting. This schema allows industry players to serve as auditors for item coherence checks (Merz et al., 2020). The experimental findings demonstrate that the suggested Acics is more cost-effective and productive than existing methods for validating the data integrity of small quantities of items. Additionally, the suggested Acics schema has lower access or write delay frequency (Alouffi et al., 2021). This architecture has not been tried on big items; thus, it may be investigated in future research.
Through creativity, businesses may build and sustain a substantial competitive edge. Therefore, it is unsurprising that businesses are interested in the systems that facilitate successful innovation. Chen et al. (2020) implied that organizations and individuals might use computer system technology for varied goals. A cloud system stores relevant information on the web and enables users to access anywhere they have internet connectivity. Many firms and individuals may reduce their investment and equipment expenses by storing and backing up their data in the cloud. Dropbox, Google Drive, and Amazon S3 are examples of cloud-based alternatives (Chen et al., 2020). Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) are executive leadership solutions that use cloud services.
Cloud system helps software engineers to examine data, identify correlations, anticipate potential problems, and support data-driven organizational decision-making. In today’s emerging world, cloud technology appears to be gaining popularity among businesses. According to Borodako et al. (2021), using cloud-based applications may assist enterprises in ensuring a smooth and efficient knowledge transfer. Despite the worldwide COVID-19 outbreak’s negative consequences, the virtualization business retained its development prospects. This issue has harmed businesses worldwide; however, most enterprises had to embrace new operating methods. Borodako et al. (2021) interviewed Chief Information Officers (CIOs) from various industries. They determined that implementing cloud computing is one of the important trends in businesses’ long-term goals for CIOs. The above notion is supported by Lal and Bharadwaj (2020), who enumerated that in 2020, cloud spending will surpass conventional IT expenditure. According to the report, 94% of participating organizations used several cloud infrastructures. Therefore, this circumstance requires enterprises to prioritize mobility and connection across clouds and administrative uniformity.
Techniques for load-balancing frameworks improve the effectiveness of large-scale technology tools and networks. Mishra et al. (2020) attribute the aforementioned fact to the capacity of cloud systems to disperse demands throughout the elements of a computing platform in a manner that assures to reduce reaction time, maximizes resource usage and efficiency, and eliminates the potential of overload. To maximize resource utilization, an effective load balancing system must eliminate capacity over-provisioning (Mishra et al., 2020). Mishra et al. (2020) emphasized numerous planning and congestion control approaches and methodologies, such as statics and dynamics. Static procedures need a previous understanding of the situation and the expectations of the programs. Nevertheless, once the algorithms begin running, these simulations cannot adjust to modifications in the surroundings or needs.
In dynamic methods, the load balancer analyzes the atmosphere and software needs during execution and tries to make modifications to disperse jobs and alter the load as required. Jena et al. (2020) identified twenty-three studies from 1954 to 2015 that concentrated on meta-heuristic methodologies as another meta-heuristic study to examine operating resource distribution strategies for boosting monetary returns and limiting cash outflows of cloud customers for IaaS in a cloud system context. Comparing these methods to conventional techniques, they determined that several strategies to improve the efficiency of meta-heuristics have been included in these schemes. In addition, none of the examined meta-heuristic procedures could produce throughput that is demonstrably better than other solutions when applied to asset distribution issues in cloud technology. Nonetheless, this survey is restricted to meta-heuristic approaches since it further examines the different ideas and scheduling methods utilized for the GaaS.
Multiple semantic developments and deployments have been deployed to cloud systems, hence semanticizing them. A conceptual cloud increases the dependability of a cloud system’s program delivery mechanisms (IaaS, PaaS, and SaaS). Taher et al. (2021) suggested a strategy that emphasized using hybrid methods that combined qualitative and quantitative approaches to solve their respective limitations. Their study aimed to enhance and expand the Semantic internet’s knowledge platform and guiding principles through a hybrid algorithm that permitted both qualitative and quantitative approaches. They used the latter to evaluate the former and make key findings about the benefits and drawbacks of the up and down strategies for locating research.
Other researchers have focused on investigating and contemplating the past and, to a lesser degree, the future. Adedugbe et al. (2020) published a modern study on semantically enhanced cloud-based IT that combines educational routes with personnel leadership skills for professional employees and administration in smart city municipalities. The provided information integrates a grammar framework with a linguistically rich structure to define the eligibility disparity for learning goals and the opportunity for municipal employees to improve their skills. Their study covers the established and recommended key computational methods for CMUTE systems. In this regard, the report offers education policy and collaborative learning, expedited and consistently applied schooling in related content matters, and technology companies’ training of smart city laborers.
In supporting Adedugbe et al.’s (2020) research findings, Iatrellis et al. (2021), in their study, concluded as enumerated below. Iatrellis et al. (2021) obtained a cloud-based solution that combines semantic architecture with a rule-based intelligent approach, Saas. The proposed solution is founded on an ecosystem-based model that combines all stakeholders explicitly or implicitly engaged in the handling management competencies of the technology platform (Iatrellis et al., 2021). They further insinuated that the cloud system linguistic paradigm would be of the essence in providing education to municipal employees who will someday work in urban mobility.
Applications
Real-world applications for cloud systems include online data storage. Organizations have a large quantity of data to keep, and the amount of this data grows over time. This information may be in any style, including text, picture, audio, and video. Enterprises do not need to put up physical storage systems to store and manage this enormous volume of data. The entire organization’s data may be divided into historical and current data (Rashid & Chaturvedi, 2019). Current data refers to data that is utilized often to accomplish day-to-day activities. Conversely, historical data refers to non-operational data with value and must be kept (Rashid & Chaturvedi, 2019). Users may now access data stored in the cloud through any global location with internet access.
Data recovery techniques that are provided by cloud systems are extensive. They provide a variety of recovery options at varied prices. Depending on their needs, businesses may choose the appropriate package. The cloud service provider may as well offer data redundancy, such as the ability to store duplicates of data in many locations. It may be a different data or server center or a different geographical area. This duplicate storage choice protects against data corruption and flexibility in retrieving data (Rashid & Chaturvedi, 2019). If data becomes unavailable at the main store site, it may be retrieved simply from other storage areas. Data redundancy alternatives include zone-redundant storage (ZRS) and geo-redundant storage (GRS).
Cloud systems are used in big data analysis in various organizations. It entails working with data sets ranging in size from zettabytes to terabytes (Wang et al., 2018). It is very challenging for any conventional database management system to retain this volume of information. Cloud computing enables the storage of enormous data sets, including structured and unstructured data from many sources and sizes (Wang et al., 2018). Not only does it supply storage but also a variety of tools for analyzing this massive amount of data. The primary objective of storing huge amounts of data is to draw something from it. The cloud’s adaptability makes it an excellent option for big data analysis. By using the cloud, companies will get a significant financial advantage since it is far less expensive than conventional large-scale big data platforms. They are no longer required to maintain massive data centers. In addition, the cloud simplifies data integration from many resources for businesses.
Cloud computing can store and access data in the medical field. As a result, it provides better access to and dissemination of information between the many medical professionals and individual patients. Furthermore, with the aid of cloud computing, offsite facilities and treatment centers, such as laboratories, and ambulances, may have their information modified remotely instead of waiting until they can reach a hospital computer (Wang et al., 2018). Additionally, cloud computing can be applied in offering entertainment services to patients and different consumers. Consequently, implementing a multi-cloud approach allows various entertainment sectors to reach their target audience. Cloud-based entertainment offers a variety of entertainment applications, including online music and video, online gaming, and video conferencing.
Cloud computing systems are used to provide cloud-based antivirus solutions. In the past, corporations installed antivirus software on their systems for protection against cyberattacks from the outside. Today, however, cloud computing offers cloud antiviral software, which is kept in the cloud that automatically monitors the system’s organization. This antivirus program detects and eliminates security threats. Sometimes they also provide the option to download the program.
Ultimately, cloud services can be used to enhance the education sector. Students are provided with e-learning and student information portals using cloud computing which has a phenomenal impact on learning (Rashid & Chaturvedi, 2019). This new trend in education offers a conducive atmosphere for learning, teaching, and experimenting to students, instructors, and researchers. In addition, everyone involved in the field can link to their organization’s cloud and get data and records from that location.
Conclusion
In conclusion, cloud systems are the internet-based provision of numerous services, such as data storage, desktops, spreadsheets, networking, and application applications. As information technology progresses rapidly, cloud solutions’ place in IT as a client storage provider has become more promising. Cloud computing has enabled businesses to rent their capabilities at an hourly rate. The performed literature review reveals the current state of cloud system development from a business perspective.
Diverse scholars have identified essential cloud system paradigms, such as SaaS, PaaS, IaaS, and CaaS. These approaches play a key role in data protection from the perspective of cloud security providers and end-users. Businesses may develop and maintain a major competitive advantage via inventiveness. For instance, Chen et al. (2020) suggested that businesses and people may use computer network innovation for various purposes. A cloud system keeps pertinent data on the internet and allows users to access it from any location with an internet connection. In addition, several semantic advancements and deployments have been implemented in cloud systems, semanticizing them.
A conceptual cloud improves the reliability of application delivery methods in a cloud system (IaaS, PaaS, and SaaS). Specifically, Taher et al. (2021) proposed a technique that used hybrid methodologies that integrated qualitative and quantitative approaches to overcome their respective constraints. Cloud systems offer comprehensive data recovery solutions. They provide many healing solutions at varying costs. In addition, several firms employ Cloud resources for big data assessment, which involves dealing with large datasets varying in size from zettabytes to terabytes.
References
Abdel-Basset, M., Mohamed, M., & Chang, V. (2018). NMCDA: A framework for evaluating cloud computing services. Future Generation Computer Systems, 86, 12-29.
Adedugbe, O., Benkhelifa, E., Campion, R., Al-Obeidat, F., Bani Hani, A., & Jayawickrama, U. (2020). Leveraging cloud computing for the semantic web: review and trends. Soft Computing, 24(8), 5999-6014.
Alouffi, B., Hasnain, M., Alharbi, A., Alosaimi, W., Alyami, H., & Ayaz, M. (2021). A systematic literature review on cloud computing security: Threats and mitigation strategies. IEEE Access, 9, 1-16.
Borodako, K., Berbeka, J., & Rudnicki, M. (2021). Technology used in knowledge management by global professional event services. Journal of Global Information Management, 29(1), 145-163.
Chen, Y., Duan, L., & Zhang, W. (2020). Effect of user involvement in supply chain cloud innovation: a game theoretical model and analysis. Journal of Global Information Management, 28(1), 23-38.
Iatrellis, O., Panagiotakopoulos, T., Gerogiannis, V. C., Fitsilis, P., & Kameas, A. (2021). Cloud computing and semantic web technologies for ubiquitous management of smart cities-related competences. Education and Information Technologies, 26(2), 2143-2164.
Jena, U. K., Das, P. K., & Kabat, M. R. (2020). Hybridization of meta-heuristic algorithm for load balancing in cloud computing environment. Journal of King Saud University-Computer and Information Sciences, 1-22.
Lal, P., & Bharadwaj, S. S. (2020). Understanding the Drivers of Cloud-Based Service Adoption and Their Impact on the Organizational Performance: An Indian Perspective. Journal of Global Information Management, 28(1), 56-85.
Merz, R., Hoch, R., & Drexel, D. (2020). A cloud-based research and learning factory for industrial production. Procedia Manufacturing, 45, 215-221.
Mishra, S. K., Sahoo, B., & Parida, P. P. (2020). Load balancing in cloud computing: A big picture. Journal of King Saud University-Computer and Information Sciences, 32(2), 149-158.
Rashid, A., & Chaturvedi, A. (2019). Cloud computing characteristics and services: A brief review. International Journal of Computer Sciences and Engineering, 7(2), 421-426.
Subramanian, N., & Jeyaraj, A. (2018). Recent security challenges in cloud computing. Computers & Electrical Engineering, 71, 28-42.
Taher, K. I., Saeed, R. H., Ibrahim, R. K., Rashid, Z. N., Haji, L. M., Omar, N., & Dino, H. I. (2021). Efficiency of semantic web implementation on cloud computing: A review. Qubahan Academic Journal, 1(3), 1-9.
Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.