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Web pages today commonly include large amounts of JavaScript code in order to offer users a dynamic experience. These scripts often make pages slow to load, partly due to a fundamental inefficiency in how browsers process JavaScript content: browsers make it easy for web developers to reason about page state by serially executing all scripts on any frame in a page, but as a result, fail to leverage the multiple CPU cores that are readily available even on low-end phones. In this paper, we show how to address this inefficiency without requiring pages to be rewritten or browsers to be modified. The key to our solution, Horcrux, is to account for the non-determinism intrinsic to web page loads and the constraints placed by the browser’s API for parallelism. Horcrux-compliant web servers perform offline analysis of all the JavaScript code on any frame they serve to conservatively identify, for every JavaScript function, the union of the page state that the function could access across all loads of that page. Horcrux’s JavaScript scheduler then uses this information to judiciously parallelize JavaScript execution on the client-side so that the end-state is identical to that of a serial execution, while minimizing coordination and offloading overheads. Across a wide range of pages, phones, and mobile networks covering web workloads in both developed and emerging regions, Horcrux reduces median browser computation delays by 31-44% and page load times by 18-37%. 

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.