Paper Title : Improving the Response Time of M-Learning and Cloud Computing Environment Using Superior Element Multitude Optimization
ISSN : 2394-2231
Year of Publication : 2021
MLA Style: Ms. N. Zahira Jahan M.C.A.,M.Phil., Ms. V.Karthika "Improving the Response Time of M-Learning and Cloud Computing Environment Using Superior Element Multitude Optimization " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
APA Style: Ms. N. Zahira Jahan M.C.A.,M.Phil., Ms. V.Karthika " Improving the Response Time of M-Learning and Cloud Computing Environment Using Superior Element Multitude Optimization " Volume 8 - Issue 2 March-April , 2021 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org
Mobile learning (M-Learning) may be a relatively new technology that helps students learn and gain knowledge using internet and Cloud computing technologies. Cloud computing is one among the recent advancements within the computing field that creates internet access easy to finish users. Many Cloud services rely on Cloud users for mapping Cloud software using virtualization techniques. Usually, Cloud users requests from various terminals will cause heavy traffic or unbalanced loads at the Cloud data centers and associated Cloud servers. Thus, a Cloud load balancer that uses an efficient load balancing technique is required altogether the cloud servers. In a new meta-heuristic algorithm named the dominant firefly algorithm that optimizes load balancing of tasks among multiple virtual machines (VMs) in the Cloud server, by improving the response efficiency of Cloud servers that concomitantly enhances the accuracy of mlearning systems. The methods and findings wont to solve load imbalance issues in Cloud servers, which can enhance the experiences of M-Learning users. Here findings such as Cloud SQL Structured Query Language, querying mechanism in mobile devices will ensure users receive their M-Learning content without delay. This method will demonstrate that by applying an effective load balancing technique would improve the throughput and the response time in mobile and cloud environments .This project proposes a resource provisioning and scheduling strategy for scientific workflows on Infrastructure as a Service (IaaS) and Platform as services clouds (PaaS). This project presents an algorithm supported the Superior Element Multitude Optimization (SEMO), which aims to attenuate the general workflow execution cost while meeting deadline constraints. The main scope of the project is employed to research best available resource within the cloud environment depend on the entire execution time and total execution cost which is compare between one process to another process. If the provider satisfies the time least time, then the method becomes to termination. Python 3.6 is employed because the front language to develop the appliance .
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—— Cloud Computing, Dominant Firefly Approach, Superior Element Multitude Optimization.