Owing to growing feature sets and sluggish improvements to smartphone CPUs (relative to mobile networks), mobile app response times have increasingly become bottlenecked on client-side computations. In designing a solution to this emerging issue, our primary insight is that app computations exhibit substantial stability over time in that they are entirely performed in rarely-updated codebases within app binaries and the OS. Building on this, we present Floo, a system that aims to automatically reuse (or memoize) computation results during app operation in an effort to reduce the amount of compute needed to handle user interactions. To ensure practicality - the struggle with any memoization effort - in the face of limited mobile device resources and the short-lived nature of each app computation, Floo embeds several new techniques that collectively enable it to mask cache lookup overheads and ensure high cache hit rates, all the while guaranteeing correctness for any reused computations. Across a wide range of apps, live networks, phones, and interaction traces, Floo reduces median and 95th percentile interaction response times by 32.7% and 72.3%.

Low interaction response times are crucial to the experience that mobile apps provide for their users. Unfortunately, existing strategies to alleviate the network latencies that hinder app responsiveness fall short in practice. In particular, caching is plagued by challenges in setting expiration times that match when a resource's content changes, while prefetching hinges on accurate predictions of user behavior that have proven elusive. We present Marauder, a system that synergizes caching and prefetching to improve the speedups achieved by each technique while avoiding their inherent limitations. Key to Marauder is our observation that, like web pages, apps handle interactions by downloading and parsing structured text resources that entirely list (i.e., without needing to consult app binaries) the set of other resources to load. Building on this, Marauder introduces two low-risk optimizations directly from the app's cache. First, guided by cached text files, Marauder prefetches referenced resources during an already-triggered interaction. Second, to improve the efficacy of cached content, Marauder judiciously prefetches about-to-expire resources, extending cache lives for unchanged resources, and downloading updates for lightweight (but crucial) text files. Across a wide range of apps, live networks, interaction traces, and phones, Marauder reduces median and 90th percentile interaction response times by 27.4% and 43.5%, while increasing data usage by only 18%.

In-network computing offers an appealing scalability trajectory for network services, as application performance scales with network devices. Despite its potential, in-network computing may not be suitable for all applications, due to paradigm assumptions and network-device limitations. As users' Internet demands keep growing, any limitations on the scalability of network services such as DNS limits the scalability of end-to-end experience. In this paper we present P4DNS, an in-network DNS solution, exploring the span and limitations of implementing a realistic network service within a network device using P4. P4DNS is a high performance DNS server, implemented in P4 over NetFPGA and providing ×52 performance improvement compared with software-based solutions. P4DNS provides insight into the limitations of implementing in-network services using today's paradigms, and the trade-offs between data and control planes.

High end networked-systems have quickly climbed from a throughput of gigabits/sec to terabits/sec, and are approaching petabits/sec. Alas, network testing equipment has not scaled: it remained either low throughput or extremely expensive. With suitable network testing equipment either not at scale or too expensive even for commercial vendors, systems may be released without proper testing and validation. We propose a methodology for large scale testing of networked systems, based on using a low-cost, open source network tester and a commodity switch. Our approach is scalable, open source, and accurate, and can be adapted to a variety of networking equipment, widely available to users.