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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. 

Wi-Fi is one of the key wireless technologies for the Internet of things (IoT) owing to its ubiquity. Low-power operation of commercial Wi-Fi enabled IoT modules (typically powered by replaceable batteries) is critical in order to achieve a long battery life, while maintaining connectivity, and thereby reduce the cost and frequency of maintenance. In this work, we focus on commonly used sparse periodic uplink traffic scenario in IoT. Through extensive experiments with a state-of-the-art Wi-Fi enabled IoT module (Texas Instruments SimpleLink CC3235SF), we study the performance of the power save mechanism (PSM) in the IEEE 802.11 standard and show that the battery life of the module is limited, while running thin uplink traffic, to ~30% of its battery life on an idle connection, even when utilizing IEEE 802.11 PSM. Focusing on sparse uplink traffic, a prominent traffic scenario for IoT (e.g., periodic measurements, keep-alive mechanisms, etc.), we design a simulation framework for single-user sparse uplink traffic on ns-3, and develop a detailed and platform-agnostic accurate power consumption model within the framework and calibrate it to CC3235SF. Subsequently, we present five potential power optimization strategies (including standard IEEE 802.11 PSM) and analyze, with simulation results, the sensitivity of power consumption to specific network characteristics (e.g., round-trip time (RTT) and relative timing between TCP segment transmissions and beacon receptions) to present key insights. Finally, we propose a standard-compliant client-side cross-layer power saving optimization algorithm that can be implemented on client IoT modules. We show that the proposed optimization algorithm extends battery life by 24%, 26%, and 31% on average for sparse TCP uplink traffic with 5 TCP segments per second for networks with constant RTT values of 25 ms, 10 ms, and 5 ms, respectively. 

Netflix is the most popular video streaming site contributing to nearly a quarter of global video traffic. Given the dominance of Netflix on Internet traffic, understanding how individual users consume content on Netflix is of interest to not only the research community, but to network operators, content creators and providers, users and advertisers. In this context, we collect Netflix viewing activity from 1060 users spanning a 1 year period, and consisting of over 1.7 million episodes and movies. We group the users based on their activity level, and provide key insights pertaining to the user’s watch patterns, watch-session length, user preferences, predictability and watch-behavior continuation tendencies. We also implement and evaluate classifiers which are used to predict the user’s engagement in a series based on their past behavioral patterns. 

Line-of-sight (LOS) is a critical requirement for mmWave wireless communications. In this work, we explore the use of access point (AP) infrastructure mobility to optimize indoor mmWave WiFi network performance based on the discovery of LOS connectivity to stations (STAs).We consider a ceiling-mounted mobile (CMM) AP as the infrastructure mobility framework. Within this framework, we present a LOS prediction algorithm based on machine learning (ML) that addresses the LOS discovery problem. The algorithm relies on the available network state information (e.g., LOS connectivity between STAs and the AP) to predict the unknown LOS connectivity status between the reachable AP locations and target STAs. We show that the proposed algorithm can predict LOS connectivity between the AP and target STAs with an accuracy up to 91%. Based on the LOS prediction algorithm, we then propose a systematic solution WiMove, which can decide if and where the AP should move to for optimizing network performance. Using both ns-3 based simulation and experimental prototype implementation, we show that the throughput and fairness performance of WiMove is up to 119% and 15% better compared with single static AP and brute force search. 

YouTube is the most popular video sharing platform with more than 2 billion active users and 1 billion hours of video content watched daily. The dominance of YouTube has had a big impact on the performance of Internet protocols, algorithms, and systems. Understanding the interaction of users with YouTube is thus of much interest to the research community. In this context, we collect YouTube watch history data from 243 users spanning a 1.5 year period. The dataset comprises of a total of 1.8 million videos. We use the dataset to analyze and present key insights about user-level usage behavior. We also show that our analysis can be used by researchers to tackle a myriad of problems in the general domains of networking and communication. We present baseline characteristics and also substantiated directions to solve a few representative problems related to local caching techniques, prefetching strategies, the performance of YouTube's recommendation engine, the variability of user's video preferences and application specific load provisioning.