3 research outputs found
Understanding Content Placement Strategies in Smartrouter-based Peer CDN for Video Streaming
Recent years have witnessed a new video delivery paradigm: smartrouter-based
peer video content delivery network, which is enabled by smartrouters deployed
at users' homes. ChinaCache (one of the largest CDN providers in China) and
Youku (a video provider using smartrouters to assist video delivery) announced
their cooperation in 2015, to create a new paradigm of content delivery based
on householders' network resources. This new paradigm is different from the
conventional peer-to-peer (P2P) approach, because millions of dedicated
smartrouters are operated by the centralized video service providers in a
coordinative manner. Thus it is intriguing to study the content placement
strategies used in a smartrouter-based content delivery system, as well as its
potential impact on the content delivery ecosystem. In this paper, we carry out
measurement studies of Youku's peer video CDN, who has deployed over 300K
smartrouter devices for its video delivery. In our measurement studies, 104K
videos were investigated and 4TB traffic has been analyzed, over controlled
smartrouter nodes and players. Our measurement insights are as follows. First,
a global content replication strategy is essential for the peer CDN systems.
Second, such peer CDN deployment itself can form an effective sub-system for
end-to-end QoS monitoring, which can be used for fine-grained request
redirection (e.g., user-level) and content replication. We also show our
analysis on the performance limitations and propose potential improvements to
the peer CDN systems.Comment: arXiv admin note: text overlap with arXiv:1605.0770
Understanding Performance of Edge Content Caching for Mobile Video Streaming
Today's Internet has witnessed an increase in the popularity of mobile video
streaming, which is expected to exceed 3/4 of the global mobile data traffic by
2019. To satisfy the considerable amount of mobile video requests, video
service providers have been pushing their content delivery infrastructure to
edge networks--from regional CDN servers to peer CDN servers (e.g.,
smartrouters in users' homes)--to cache content and serve users with storage
and network resources nearby. Among the edge network content caching paradigms,
Wi-Fi access point caching and cellular base station caching have become two
mainstream solutions. Thus, understanding the effectiveness and performance of
these solutions for large-scale mobile video delivery is important. However,
the characteristics and request patterns of mobile video streaming are unclear
in practical wireless network. In this paper, we use real-world datasets
containing 50 million trace items of nearly 2 million users viewing more than
0.3 million unique videos using mobile devices in a metropolis in China over 2
weeks, not only to understand the request patterns and user behaviors in mobile
video streaming, but also to evaluate the effectiveness of Wi-Fi and
cellular-based edge content caching solutions. To understand performance of
edge content caching for mobile video streaming, we first present temporal and
spatial video request patterns, and we analyze their impacts on caching
performance using frequency-domain and entropy analysis approaches. We then
study the behaviors of mobile video users, including their mobility and
geographical migration behaviors. Using trace-driven experiments, we compare
strategies for edge content caching including LRU and LFU, in terms of
supporting mobile video requests. Moreover, we design an efficient caching
strategy based on the measurement insights and experimentally evaluate its
performance.Comment: 13 pages, 19 figure
Towards Wi-Fi AP-Assisted Content Prefetching for On-Demand TV Series: A Reinforcement Learning Approach
The emergence of smart Wi-Fi APs (Access Point), which are equipped with huge
storage space, opens a new research area on how to utilize these resources at
the edge network to improve users' quality of experience (QoE) (e.g., a short
startup delay and smooth playback). One important research interest in this
area is content prefetching, which predicts and accurately fetches contents
ahead of users' requests to shift the traffic away during peak periods.
However, in practice, the different video watching patterns among users, and
the varying network connection status lead to the time-varying server load,
which eventually makes the content prefetching problem challenging. To
understand this challenge, this paper first performs a large-scale measurement
study on users' AP connection and TV series watching patterns using
real-traces. Then, based on the obtained insights, we formulate the content
prefetching problem as a Markov Decision Process (MDP). The objective is to
strike a balance between the increased prefetching&storage cost incurred by
incorrect prediction and the reduced content download delay because of
successful prediction. A learning-based approach is proposed to solve this
problem and another three algorithms are adopted as baselines. In particular,
first, we investigate the performance lower bound by using a random algorithm,
and the upper bound by using an ideal offline approach. Then, we present a
heuristic algorithm as another baseline. Finally, we design a reinforcement
learning algorithm that is more practical to work in the online manner. Through
extensive trace-based experiments, we demonstrate the performance gain of our
design. Remarkably, our learning-based algorithm achieves a better precision
and hit ratio (e.g., 80%) with about 70% (resp. 50%) cost saving compared to
the random (resp. heuristic) algorithm