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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Title: Time-shifted Prefetching and Edge-caching of Video Content to Reduce Peak-time Network Traffic
Committee:
Dr. Sivakumar, Advisor
Dr. Fekri, Chair
Dr. Blough
Abstract: The objective of the proposed research is to provide insights into video content consumption, and develop a set of data-driven prediction and prefetching algorithms, based on machine-learning and deep-learning techniques, which accurately anticipates the video content the user will consume, and caches it on edge nodes during off-peak periods to reduce peak-time usage. Video streaming accounts for over 60% of global fixed downstream Internet traffic and 65% of worldwide mobile downstream traffic; and is expected to grow to 82% by 2022. As a result of the increasing growth and popularity of video content, the network is heavily burdened. Typically, upgrades are triggered when there is a reasonably sustained peak usage that exceeds 80% of capacity. In this context, with network traffic load being significantly higher during peak periods (up to 5x as much), we explore the problem of prefetching video content during off-peak periods of the network even when such periods are substantially separated from the actual usage-time. To this end, we collect and perform an in-depth analysis on real-world datasets of YouTube and Netflix usage collected from over 1,200 users. Equipped with the datasets and our derived insights, we develop a set of data-driven prediction and prefetching algorithms, based on machine-learning and deep-learning techniques, which anticipates the video content the user will consume, and prefetches it during off-peak periods to reduce peak-time usage.
