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%.

Data-intensive scalable computing (DISC) systems such as Google’s MapReduce, Apache Hadoop, and Apache Spark are being leveraged to process massive quantities of data in the cloud. Modern DISC applications pose new challenges in exhaustive, automatic testing because they consist of dataflow operators, and complex user-defined functions (UDF) are prevalent unlike SQL queries. We design a new white-box testing approach, called BigTest to reason about the internal semantics of UDFs in tandem with the equivalence classes created by each dataflow and relational operator. Our evaluation shows that, despite ultra-large scale input data size, real world DISC applications are often significantly skewed and inadequate in terms of test coverage, leaving 34% of Joint Dataflow and UDF (JDU) paths untested. BigTest shows the potential to minimize data size for local testing by 10^5 to 10^8 orders of magnitude while revealing 2X more manually-injected faults than the previous approach. Our experiment shows that only few of the data records (order of tens) are actually required to achieve the same JDU coverage as the entire production data. The reduction in test data also provides CPU time saving of 194X on average, demonstrating that interactive and fast local testing is feasible for big data analytics, obviating the need to test applications on huge production data.