PRICE DEMAND MODEL FOR A CLOUD CACHE

Cloud applications that offer data management services are emerging. Such clouds support caching of data in order to provide quality query services. The users can query the cloud data, paying the price for the infrastructure they use. Cloud management necessitates an economy that manages the service of multiple users in an efficient, but also, resource economic way that allows for cloud profit. Naturally, the maximization of cloud profit given some guarantees for user satisfaction presumes an appropriate price-demand model that enables optimal pricing of query services. The model should be plausible in that it reflects the correlation of cache structures involved in the queries. Optimal pricing is achieved based on a dynamic pricing scheme that adapts to time changes. This paper proposes a novel price-demand model designed for a cloud cache and a dynamic pricing scheme for queries executed in the cloud cache. The pricing solution employs a novel method that estimates the correlations of the cache services in an time-efficient manner. The experimental study shows the efficiency of the solution

Applying Learning Analytics for Designing Effective Pedagogy for Online Courses: Analysis and Recommendations

The countrywide lockdown since March 2020 due to COVID 19 pandemic has brought drastic changes in the Indian education system. Today, many higher education institutions offer online delivery as an alternative and/or addition to provide more flexibility to learners, specifically in the current COVID 19 Pandemic. The conventional teaching method to the technology-driven virtual mode of teaching provided opportunities with challenges to academic stakeholders. Now with the new session, discussions on the reopening of educational institutions are going on. Hence, it is time to review the learning that took place during this pandemic situation. Learners being confronted with such services come with different expectations of what that means to their learning paths and behaviors. Learning Analytics is a relatively new and innovative way of making learner behavior and performances explicit by analyzing significant learners' feedback data. In this study, we take the case of online courses offered by various educational institutions all over India, and the analysis encompasses the population of learners of the online courses during the COVID 19 Pandemic period. Primary data has been collected using Google form when journals, reports, and websites are secondary data collection sources. We classified the data into distinctive parts: the overall learning experience of this course, the fulfillment of the learners' objectives, the difficulty of the assignments, the quality of the material supplied, the difficulty level of the course, the quality of the live session, opinion about the virtual approach of teaching, opinion about the contribution of virtual academic programs on knowledge building, opinion about the impact of virtual mode of teaching on performance, and opinion about the causes affecting students’ performance in the virtual mode of learning. The analysis outcomes will guide the host institutions and other similar institutions to design their pedagogy for future online courses more effectively, influencing learner engagement and retention