Abstract-The transmission of video content accounts for a large share of today's Internet traffic. While Video-on-Demand (VoD) substantially contributes to this, live streaming events such as video broadcasts from the Olympic Games can cause very high traffic volumes in the short term as well. Such peaks along with high fluctuations triggered by sudden changes in the behavior of users make the design of live streaming systems particularly challenging. Peer-to-Peer (P2P) has proven to be a scalable approach for disseminating content to a large number of users. Accordingly, the body of research offers numerous P2P live streaming approaches tailored towards specific scenarios and assumptions. However, no single approach is able to perform well under all possible conditions. Keeping up a high performance when conditions are changing is a challenge, since topology management and scheduling mechanisms cannot be exchanged easily. Therefore, this paper proposes TRANSIT, a new approach going beyond existing works in that it supports transitions between different live streaming mechanisms. TRANSIT makes different configurations of such mechanisms seamlessly exchangeable to enable the optimal choice of configurations for a wide range of live streaming scenarios. The approach is evaluated using measurement-and trace-based workloads. The results show that TRANSIT is able to maintain a high performance at a low overhead in highly fluctuating environments, whereas static configurations show serious performance degradations.
In March 2020, the World Health Organization declared the Corona Virus 2019 (COVID-19) outbreak a global pandemic. As a result, billions of people were either encouraged or forced by their governments to stay home to reduce the spread of the virus. This caused many to turn to the Internet for work, education, social interaction, and entertainment. With the Internet demand rising at an unprecedented rate, the question of whether the Internet could sustain this additional load emerged. To answer this question, this paper will review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance. In order to keep our study broad, we collect and analyze Internet traffic data from multiple locations at the core and edge of the Internet. From this, we characterize how traffic and application demands change, to describe the "new normal," and explain how the Internet reacted during these unprecedented times.
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