<p dir="ltr">Ensuring a high quality of experience for Internet applications is challenging owing to the significant variability (e.g., of traffic patterns) inherent to both cloud data-center networks and wide area networks. This thesis focuses on optimizing application performance by both conducting measurements to characterize traffic variability, and designing applications that can perform well in the face of variability. On the data center side, a key aspect that impacts performance is traffic burstiness at fine granular time scales. Yet, little is know about traffic burstiness and how it impacts application loss. On the wide area side, we focus on video applications as a major traffic driver. While optimizing traditional videos traffic remains a challenge, new forms of video such as 360◦ introduce additional challenges such as respon- siveness in addition to the bandwidth uncertainty challenge. In this thesis, we make three contributions.</p><p dir="ltr"><b>First</b>, for data center networks, we present Millisampler, a lightweight network traffic char- acterization tool for continual monitoring which operates at fine configurable time scales, and deployed across all servers in a large real-world data center networks. Millisampler takes a host-centric perspective to characterize traffic across all servers within a data center rack at the same time. Next, we present data-center-scale joint analysis of burstiness, contention, and loss. Our results show (i) bursts are likely to encounter contention; (ii) contention varies significantly over short timescales; and (iii) higher contention need not lead to more loss, and the interplay with workload and burst properties matters.</p><p dir="ltr"><b>Second</b>, we consider challenges with traditional video in wide area networks. We take a step towards understanding the interplay between Content-Delivery-Networks (CDNs), and video performance through end-to-end measurements. Our results show that (i) video traffic in a session can be sourced from multiple CDN layers, and (ii) throughput can vary signifi- cantly based on the traffic source. Next we evaluate the potential benefits of exposing CDN information to the client Adaptive-Bit-Rate (ABR) algorithm. Emulation experiments show the approach has the potential to reduce prediction inaccuracies, and enhance video quality of experience (QoE).</p><p dir="ltr"><b>Third</b>, for 360◦ videos, we argue for a new streaming model which is explicitly designed for continuous, rather than stalling, playback to preserve interactivity. Next, we propose Dragonfly, a new 360° system that leverages the additional degrees of freedom provided by this design point. Dragonfly proactively skips tiles (i.e., spatial segment of the video) using a model that defines an overall utility function that captures factors relevant to user experience. We conduct a user study which shows that majority of interactivity feedback indicating Dragonfly being highly reactive, while the majority of state-of-the-art’s feedback indicates the systems are slow to react. Further, extensive emulations show Dragonfly improves the image quality significantly without stalling playback.</p>