Faculty Publications
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Item Cloud-Based E-Learning Service: Insight from India(Springer, 2020) Vanitha, P.S.; Alathur, S.The factors influencing the adoption of a cloud platform in the development of e-learning service is identified in this study. The e-learning service based on a cloud platform is analyzed from single/multiple data center dimensions. In spite of developing e-learning infrastructure, the service of the cloud platform is often adopted. The cloud-based e-learning simulation environment is created using CloudAnalyst tool. The efficiency of the cloud is analyzed based on e-learning hosted on a single data center and multiple data centers. The service time and overall response time of the datacenter are analyzed through CloudAnalyst. The infrastructure cost estimation for both models is also calculated. The study identifies that these factors influence the development of e-learning services in a cloud platform. Earlier studies less analyze the influencing factors of the online courses in the cloud environment. In future research, the cost factor can also be considered in the development of e-learning services. Fewer studies are reported on e-learning service based on a cloud platform in the Indian context. The current study demonstrates how cloud Infrastructure as a Service (IaaS) improves the performance of the e-learning system. © Springer Nature Singapore Pte Ltd 2020.Item Optimal load balancing in cloud computing by efficient utilization of virtual machines(2014) Domanal, S.G.; Guddeti, G.R.M.Load balancing is the major concern in the cloud computing environment. Cloud comprises of many hardware and software resources and managing these will play an important role in executing a client's request. Now a day's clients from different parts of the world are demanding for the various services in a rapid rate. In this present situation the load balancing algorithms built should be very efficient in allocating the request and also ensuring the usage of the resources in an intelligent way so that underutilization of the resources will not occur in the cloud environment. In the present work, a novel VM-assign load balance algorithm is proposed which allocates the incoming requests to the all available virtual machines in an efficient manner. Further, the performance is analyzed using Cloudsim simulator and compared with existing Active-VM load balance algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all available virtual machines without under/over utilization. © 2014 IEEE.Item A novel bio-inspired load balancing of virtualmachines in cloud environment(Institute of Electrical and Electronics Engineers Inc., 2015) Ashwin, T.S.; Domanal, S.G.; Guddeti, G.R.M.Load Balancing plays an important role in managing the software and the hardware components of cloud. In this present scenario the load balancing algorithm should be efficient in allocating the requested resource and also in the usage of the resources so that the over/underutilization of the resources will not occur in the cloud environment. In the present work, the allocation of all the available Virtual Machines is done in an efficient manner by Particle Swarm Optimization load balancing algorithm. Further, we have used cloudsim simulator to compare and analyze the performance of our algorithm. Simulation results demonstrate that the proposed algorithm distributes the load on all the available virtual machines uniformly i.e, without any under/over utilization and also the average response time is better compared to all existing scheduling algorithms. © 2014 IEEE.