An Effective Cloud Downloading Service Using Adaptive Mode Selection Algorithm

Authors

  • S. Prasanna Research Scholar, Department of Computer Science and Engineering, Engineering, St. Peter's Institute of Higher Education and Research, Research, Avadi, Chennai, Tamilnadu, India
  • B. Shanthini Prof & Head Department of Computer Science and St. Peter's Institute of Higher Education and Avadi, Chennai, Tamilnadu, India

Keywords:

Peer to Peer Networks, Cloud Downloading, Content Delivery Network, EAMS Algorithm

Abstract

A traditional model in peer to peer video content downloading over the internet is rapidly growing with respect to the number of user the intensity of their use, and the diversity of content being accessed. Therefore, it is important to design video content downloading system that effectively scales with respect to both the request rate and the number of available content items. Peer to Peer is the common technique for cloud downloading system. With this approach, none of your co-workers create a video call, and that content is only shared with other co-workers. Therefore, throughput increases mechanically as the population of peer entities increases. Scalability has been a bonus of peer-to-peer feedback, but everyone's request volume has increased, and report overload isn't always guaranteed. In this paper we develop a technique to enhance the downloading time with aid of eye Os and Content Distribution Techniques. And also that system using two philosophies in cloud either as a server or a server to suit it to any it any operating system scenarios. And our Enhanced Automatic Mode Selection algorithm automatically selects the mode compared with bandwidth and cache size.

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Published

05.12.2023

How to Cite

Prasanna, S. ., & Shanthini, B. . (2023). An Effective Cloud Downloading Service Using Adaptive Mode Selection Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(7s), 171–178. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4055

Issue

Vol. 12 No. 7s (2024)

Section

Research Article

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