Cloud Virtualization Use Case and Solution Recommendation
The innovations brought by cloud technology are in a way that modern businesses can’t do without it as it is now a must-have to stay competitive and efficient in the digitally oriented world we live in today. Conventional on premises IT infrastructure is not suitable any more as companies grow larger and increasingly depend on data and digital services, becoming too expensive, rigid or exposed. Cloud virtualization is a great solution that helps companies use cloud virtual resources, better manage the data, and lower the operational feet. In this paper, the use of a business case of a small market of a rapidly growing marketing agency is presented, and how cloud virtualization can help to address the current technology limit of the agency. The paper will recommend a particular cloud platform after outlining the company’s situation and its technological needs, and defend the recommendation made with research collected during the course.
The agency in question is BrightWave Marketing, a digital marketing agency that performs social media campaigns, content creation, and data driven advertising for small and mid-sized clients. During the past year, BrightWave has doubled its clients and has added video production and advanced analytics to our services. That said, the company’s infrastructure can not keep up (Jenkins, 2019). BrightWave is a thin office with a couple of physical servers to handle project files, manage our internal applications and scale a database of customer insights. These servers are becoming old, invalidating less and less, constantly asking for maintenance. Furthermore, there is also a risk to the business due to the lack of offsite backup as there is limited storage capacity. BrightWave has realized that continuing to spend on local hardware cannot be sustained. Physical space is limited and the cost of replacing or extending on premises infrastructure is very high. More employees are working remotely now than ever before, and as a result, more people need secure and easy to access storage in a centralized form. Additionally, the company is now obligated to split its outdated systems to comply with the demands of data privacy regulations in various states. Machine learning tools, although outside of the capabilities offered by their current infrastructure, are something their team is also interested in exploring for performing their campaign performance analysis more deeply.
Cloud Virtualization
Cloud virtualization and cloud storage turned out to be the best fit for these needs. Cloud based storage would give an added advantage of security, accessibility and scalability, whereas Virtualization would help the BrightWave to reduce or completely cut any dependability on physical setup (Mangalampalli et al, 2023). Selecting the right cloud provider from a list of three top (Amazon Web Services, AWS; Microsoft Avez; Google Cloud Platform (GCP)) is the main focus of the company. Affordability, ease of integration, performance, security, and support for advanced analytics are strengths and limitations of each that have to be weighed in terms of where BrightWave is headed. The company with the largest and most mature cloud provider in the market is Amazon Web Services (AWS). It provides a variety of virtualization services through Amazon EC2 and scalable storage solutions which include S3 and EBS. As we know, AWS has a very powerful infrastructure, is available globally, and has a great flexibility. Another added value is the set of AI and machine learning tools such as Amazon Sage Maker that are aligned with BrightWave’s future data analysis plans (Hardt et al,2021). But AWS pricing can be complicated and monthly costs are hard to predict if you aren’t already an experienced cloud cost manager with a firm grip on your monthly bills. For BrightWave’s small IT department, AWS also has a steeper learning curve if they all lack in house cloud expertise.
Cloud Virtualization
Being integrated to widely used Microsoft products such as Windows Server, Office 365, and Active Directory, Microsoft Azure has an edge. The move to Azure would be relatively easy as BrightWave already makes extensive use of a variety of Microsoft technologies. BrightWave needed a server virtualization and storage service that was both scalable and easy to maintain, and both Azure Virtual Machines and Azure Blob Storage would fit that bill. It also has very strong compliance certifications, which is necessary to meet a client’s data privacy needs (Soh et al, 2020). Additionally, Azure supplies tools including Azure Synapse and Machine Learning Studio for data analysis that will complement BrightWave’s intent to enhance their marketing tactics with additional in depth surveys. Azure has a nice interface, cheap price for small businesses and excellent customer support options. Google Cloud Platform (GCP) excels in analytics and big data, with tools like BigQuery and AutoML. Further, it would provide BrightWave with reliable cloud storage and virtualization services to fulfill its needs. GCP, though, is generally considered more developer oriented and generally has less tie in with BrightWave’s current setup (Gupta et al, 2021). It’s purchased at a competitive price and performs well in the analytics side, but lacks the hybrid cloud options, enterprise support on the like of Azure and AWS. It makes it a somewhat problematic choice for a business that needs to get straight to work with an undertaking that gives quick reliability and simple migration from their current condition.
Taking in all these factors, Microsoft Azure is the best option BrightWave Marketing. Because the platform integrates so tightly with the Microsoft ecosystem, the transition is that much easier and less likely to have any compatibility problems (Soh et al, 2020). BrightWave would be able to consolidate server needs into a scalable cloud environment bearing less administrative overhead using Azure’s virtualization services. Its storage options are secure and flexible for individuals to share files from any place and keep monitor over permissions and information sharing. It would be straight beneficial to the company’s remote workforce and distant clients. Because BrightWave can also start using machine learning and data analytics tools to build more sophisticated client reports and performance dashboards in the future, we can integrate that into our Azure ecosystem and out of Azure. Another good justification for Azure is having hybrid cloud capabilities. All of a sudden, BrightWave may not be ready to give up all on premise systems at once. The company can hold some local services yet transition others to the cloud with Azure. Overall, this hybrid model reduces the disruption and provides a reasonable degree of comfort and control for a new cloud services facing organization. Transparency and flexibility in the pricing model for Azure is very important to BrightWave’s budget conscious operation from a financial standpoint.
Cloud Virtualization
In conclusion, BrightWave Marketing’s current infrastructure is not supporting it to realize its potential of growth. Their outdated servers and storage systems have serious risk to future success in the form of costs, limitations, and risks. With Microsoft Azure, BrightWave can modernize its operations, enhance the security and accessibility of its systems, and set the stage to bring about more advanced service offerings. It works with all of the company’s key requirements and provides room for growth with low upfront investment and a minimal learning curve. BrightWave can do both operations efficient and innovations through cloud virtualization and storage technologies.
References
Jenkins, S. (2019, June). BrightWave. Only Influencers. https://www.onlyinfluencers.com/top-email-agencies/brightwave
Mangalampalli, S., Sree, P. K., Swain, S. K., & Karri, G. R. (2023). Cloud computing and virtualization. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation, 13-40.
Hardt, M., Chen, X., Cheng, X., Donini, M., Gelman, J., Gollaprolu, S., … & Kenthapadi, K. (2021, August). Amazon sagemaker clarify: Machine learning bias detection and explainability in the cloud. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 2974-2983).
Soh, J., Copeland, M., Puca, A., Harris, M., Soh, J., Copeland, M., … & Harris, M. (2020). Overview of azure infrastructure as a service (IaaS) services. Microsoft Azure: Planning, Deploying, and Managing the Cloud, 21-41.
Gupta, B., Mittal, P., & Mufti, T. (2021, March). A review on amazon web service (aws), microsoft azure & google cloud platform (gcp) services. In Proceedings of the 2nd International Conference on ICT for Digital, Smart, and Sustainable Development, ICIDSSD 2020, 27-28 February 2020, Jamia Hamdard, New Delhi, India (p. 9).
