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Internet traffic load is not uniformly distributed through the day; it is significantly higher during peak-periods, and comparatively idle during off-peak periods. In this context, we present CacheFlix, a time-shifted edge-caching solution that prefetches Netflix content during off-peak periods of network connectivity. We specifically focus on Netflix since it contributes to the largest percentage of global Internet traffic by a single application. We analyze a real-world dataset of Netflix viewing activity that we collected from 1060 users spanning a 1-year period and comprised of over 2.2 million Netflix TV shows and documentary series; we restrict the scope of our study to Netflix series that account for 65% of a typical user's Netflix load in terms of bytes fetched. We present insights on users' viewing behavior, and develop an accurate and efficient prediction algorithm using LSTM networks that caches episodes of Netflix series on storage constrained edge nodes, based on the user's past viewing activity. We evaluate CacheFlix on the collected dataset over various cache eviction policies, and find that CacheFlix is able to shift 70% of Netflix series traffic to off-peak hours.
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MANTIS: time-shifted prefetching of YouTube videos to reduce peak-time cellular data usage
The load on wireless cellular networks is not uniformly distributed through the day, and is significantly higher during peak periods. In this context, we present MANTIS, a time-shifted prefetching solution that prefetches content during off-peak periods of network connectivity. We specifically focus on YouTube given that it represents a significant portion of overall wireless data-usage. We make the following contributions: first, we collect and analyze a real-life dataset of YouTube watch history from 206 users comprised of over 1.8 million videos spanning over a 1-year period and present insights on a typical user's viewing behavior; second, we develop an accurate prediction algorithm using a K-nearest neighbor classifier approach; third, we evaluate the prefetching algorithm on two different datasets and show that MANTIS is able to reduce the traffic during peak periods by 34%; and finally, we develop a proof-of-concept prototype for MANTIS and perform a user study.
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- Award ID(s):
- 1813242
- PAR ID:
- 10173556
- Date Published:
- Journal Name:
- MMSys '20: Proceedings of the 11th ACM Multimedia Systems Conference
- Page Range / eLocation ID:
- 112 to 125
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3339825.3391864
