Video traffic already represents a significant fraction of today's traffic and is projected to exceed 90% in the next five years. In parallel, user expectations for a high quality viewing experience (e.g., low startup delays, low buffering, and high bitrates) are continuously increasing. Unlike traditional workloads that either require low latency (e.g., short web transfers) or high average throughput (e.g., large file transfers), a high quality video viewing experience requires sustained performance over extended periods of time (e.g., tens of minutes). This imposes fundamentally different demands on content delivery infrastructures than those envisioned for traditional traffic patterns. Our large-scale measurements over 200 million video sessions show that today's delivery infrastructure fails to meet these requirements: more than 20% of sessions have a rebuffering ratio ≥ 10% and more than 14% of sessions have a video startup delay ≥ 10s. Using measurement-driven insights, we make a case for a video control plane that can use a global view of client and network conditions to dynamically optimize the video delivery in order to provide a high quality viewing experience despite an unreliable delivery infrastructure. Our analysis shows that such a control plane can potentially improve the rebuffering ratio by up to 2× in the average case and by more than one order of magnitude under stress.

As the distribution of the video over the Internet becomes main- stream and its consumption moves from the computer to the TV screen, user expectation for high quality is constantly increasing. In this context, it is crucial for content providers to understand if and how video quality affects user engagement and how to best invest their resources to optimize video quality. This paper is a first step towards addressing these questions. We use a unique dataset that spans different content types, including short video on demand (VoD), long VoD, and live content from popular video con- tent providers. Using client-side instrumentation, we measure quality metrics such as the join time, buffering ratio, average bitrate, rendering quality, and rate of buffering events.

We consider two problems related to I/O: First, find the minimum primary memory size required to factor a sparse, symmetric matrix when permitted to read and write the data exactly once. Second, find the minimum data traffic between core and external memory when permitted to read and write the data many times. These problems are likely to be intractable in general, but we prove upper and lower bounds on these quantities for several model problems with useful sparsity (i.e., whose computational graphs have small separators). We provide fast algorithms for computing these quantities through simulation for irregular problems. The choice of factorization algorithms (left-looking, right-looking, multifrontal), orderings (nested dissection or minimum degree), and blocking techniques (1- or 2- dimensional blocks) can change the memory size and traffic by orders of magnitude. Explicitly moving the data (files managed by the program) improves performance significantly over implicit data movement (pages managed by the operating system). Thus this work guides us in designing a software library that implements an external memory sparse solver.