Faculty Publications
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Publications by NITK Faculty
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Item Developing Software for Cloud: Opportunities and Challenges for Developers(wiley, 2016) Chandrasekaran, K.; Marimuthu, C.Software as a service (SaaS) is emerging as a widely used software delivery model, which is receiving a lot of attention across industry and academia. It is not only a software delivery model; it is also a business model that allows the SaaS provider to make money and SaaS consumer to save money. It gives rise to a lot of challenges and research opportunities in SaaS development, which will be discussed in this chapter. The chapter begins with basic understanding of cloud computing and SaaS followed by a discussion of the challenges and research opportunities to address them when developing SaaS. Then it covers the popular SaaS development platforms available for public cloud and private cloud followed by multitenancy at database level to secure the user data on cloud platforms. Finally, this chapter presents the best practices to transform traditional Web applications to cloud-based multitenant SaaS applications. © 2016 John Wiley & Sons, Ltd.Item Cloud Services and Service Providers(wiley, 2016) Chandrasekaran, K.; Ananth, A.Cloud computing provisions resources to consumers in the form of different services like software, infrastructure, platform, and more. Many companies have come forward to offer cloud services. This chapter provides an overview of cloud services offered by various major providers such as Amazon, Microsoft, Google, EMC, Salesforce.com and IBM. They provide various tools and services in order to give cloud support for their customers. Each section briefly describes cloud services offered by a provider and their features, and identifies tools and technologies adopted by the company in order to provide services to the users. This chapter helps readers to distinguish among different services provided by various companies and make appropriate choices to suit their requirements. © 2016 John Wiley & Sons, Ltd.Item Enhancing Data Quality in Hybrid Cloud Architectures(Institute of Electrical and Electronics Engineers Inc., 2024) Fernandes, G.H.; Divakarla, U.; Chandrasekaran, K.The emergence of the hybrid cloud model has completely changed how businesses handle, store and use their data. A common option for businesses looking to combine the benefits of cloud services and on-premises infrastructure is the hybrid cloud strategy. However, because of the complexity of data management across contexts and the integration of disparate systems, this paradigm presents serious hurdles to preserving data quality. Since data is the primary source for many important business decisions, ensuring data quality - which includes correctness, consistency, completeness, security, and reliability - remains a top priority. This study presents a unique method that makes use of cloud computing and machine learning (ML) algorithms to improve data quality in hybrid cloud environments. The detection, prevention and remediation of data quality issues by integrating state-of-the-art machine learning techniques into hybrid cloud systems is thoroughly examined in this study. The suggested architecture seeks to deliver more dependable and trustworthy data for decision-making processes by offering real-time monitoring, analysis and quality enhancement of data throughout the hybrid infrastructure. The efficacy of methodology in tackling data quality issues in hybrid cloud settings is illustrated using experiments and case studies. © 2024 IEEE.Item Ensemble deep neural network based quality of service prediction for cloud service recommendation(Elsevier B.V., 2021) Sahu, P.; Raghavan, S.; Chandrasekaran, K.Applications of Cloud Services are increasing day by day, and so is the difficulty of choosing the best-suited service for a customer. Quality of Service (QoS) parameters can be used for quality assurance and evaluation; further, a service can be recommended based on these QoS parameters’ values. Recommendation systems are getting much attention lately. It has a crucial role in almost all the major commercial platforms and many improvements are being made to make the recommendations more precise and closer to the user's requirements. Conventional Machine Learning algorithms and statistical analysis methods, presently are not that efficient in learning the complex correlation between data elements. Lately, Deep Learning models have proven to be practical and precise in areas like natural language processing, image processing, data mining, & data interpretation. However, there are not many examples of complete Deep Learning applications for cloud service recommendation systems, though some works partially use Deep Learning. We propose the Ensemble of Deep Neural Networks (EDNN) method, which is of the hybrid type, i.e., the fusion of neighborhood-based and neural network model-based methods. The output obtained from both the models are combined using another different neural network model. Our approach for predicting QoS values is simple and different from previous works, and the results show that it outperforms other classical methods marginally. © 2021 Elsevier B.V.