Cloud-based financial exchanges require sub-10ns device-to-device clock synchronization accuracy while adhering to Coordinated Universal Time (UTC). Existing clock sync techniques struggle to meet this demand at scale and are vulnerable to clock drift, jitter, and path asymmetries. Firefly, a software-driven datacenter clock sync system, scalably, cost-effectively, and reliably achieves very high clock sync accuracy. It employs a distributed consensus algorithm on a random overlay graph to rapidly converge to a common time while applying gradual adjustments to device hardware clocks. To realize consistent sync-to-UTC (external sync) across devices while maintaining a stable device-to-device internal sync, Firefly uses a novel technique, layered synchronization, that decouples internal and external syncs. In a 248-machine Clos network, Firefly achieves sub-10ns device-to-device and ≤1μs device-to-UTC sync, and is resilient to time server failure and unstable clocks.

Heuristics are ubiquitous in computer systems. Examples include congestion control, adaptive bit rate streaming, scheduling, load balancing, and caching. In some domains, theoretical proofs have provided clarity on the conditions where a heuristic is guaranteed to work well. This has not been possible in all domains because proving such guarantees can involve combinatorial reasoning making it hard, cumbersome and error-prone. In this paper we argue that computers should help humans with the combinatorial part of reasoning. We model reasoning questions as ∃∀ formulas [1] and solve them using the counterexample guided inductive synthesis (CEGIS) framework. As preliminary evidence, we prototype CCmatic, a tool that semi-automatically synthesizes congestion control algorithms that are provably robust. It rediscovered a recent congestion control algorithm that provably achieves high utilization and bounded delay under a challenging network model. It also found previously unknown variants of the algorithm that achieve different throughput-delay trade-offs.

Recent congestion control research has focused on purpose-built algorithms designed for the special needs of specific applications. Often, limited testing before deploying a CCA results in unforeseen and hard-to-debug performance issues due to the complex ways a CCA interacts with other existing CCAs and diverse network environments. We present CC-Fuzz, an automated framework that uses genetic search algorithms to generate adversarial network traces and traffic patterns for stress-testing CCAs. Initial results include CC-Fuzz automatically finding a bug in BBR that causes it to stall permanently, and automatically discovering the well-known low-rate TCP attack, among other things.

Real-time interactive video streaming applications like cloud-based video games, AR, and VR require high quality video streams and extremely low end-to-end interaction delays. These requirements cause the QoE to be extremely sensitive to packet losses. Due to the inter-dependency between compressed frames, packet losses stall the video decode pipeline until the lost packets are retransmitted (resulting in stutters and higher delays), or the decoder state is reset using IDR-frames (lower video quality for given bandwidth). Prism is a hybrid predictive-reactive packet loss recovery scheme that uses a split-stream video coding technique to meet the needs of ultra-low latency video streaming applications. Prism's approach enables aggressive loss prediction, rapid loss recovery, and high video quality post-recovery, with zero overhead during normal operation - avoiding the pitfalls of existing approaches. Our evaluation on real video game footage shows that Prism reduces the penalty of using I-frames for recovery by 81%, while achieving 30% lower delay than pure retransmission-based recovery.

Social live video streaming (SLVS) applications are becoming increasingly popular with the rise of platforms such as Facebook-Live, YouTube-Live, Twitch and Periscope. A key characteristic that differentiates this new class of applications from traditional live streaming is that these live streams are watched by viewers at different delays; while some viewers watch a live stream in real-time, others view the content in a time-shifted manner at different delays. In the presence of variability in the upload bandwidth, which is typical in mobile environments, existing solutions silo viewers into either receiving low latency video at a lower quality or a higher quality video with a significant delay penalty, without accounting for the presence of diverse time-shifted viewers.

Various trends are reshaping Internet video delivery: exponential growth in video traffic, rising expectations of high video quality of experience (QoE), and the proliferation of varied content delivery network (CDN) deployments (e.g., cloud computing-based, content provider-owned datacenters, and ISP-owned CDNs). More fundamentally though, content providers are shifting delivery from a single CDN to multiple CDNs, through the use of a content broker. Brokers have been shown to invalidate many traditional delivery assumptions (e.g., shifting traffic invalidates short- and long-term traffic prediction) by not communicating their decisions with CDNs. In this work, we analyze these problems using data from a CDN and a broker. We examine the design space of potential solutions, finding that a marketplace design (inspired by advertising exchanges) potentially provides interesting tradeoffs. A marketplace allows all CDNs to profit on video delivery through fine-grained pricing and optimization, where CDNs learn risk-adverse bidding strategies to aid in traffic prediction. We implement a marketplace-based system (which we dub Video Delivery eXchange or VDX) in CDN and broker data-driven simulation, finding significant improvements in cost and data-path distance.