Software rollout is the process of replacing the version of an application that is currently running in production with a new version. Many subtle and catastrophic bugs occur during software rollout. There are many existing techniques to improve the odds of a rollout completing successfully, but these techniques don't work well when the application has shared, persistent, mutable state. In this paper, we present a practical framework to test the rollout of stateful applications. Our framework uses diff testing to verify that the new version of an application behaves identically to the currently running version that will be replaced. The framework has three main components to safely and efficiently compare the behavior of the two versions. First, we implement database branching on top of Postgres. Second, we implement an efficient algorithm to diff two database branches. Third, we describe how to replay client requests to improve test coverage. Finally, we identify three common categories of rollout bugs and demonstrate how our framework can find these bugs with minimal performance overhead.

Knowing expected milk yield can help dairy farmers in better decision-making and management.The objective of this study was to build and compare predictive models to forecast daily milk yield over a long duration.A machine-learning pipeline was provided and five baseline models as well as a novel stacking model were developed for the prediction of milk yield on the CowNflow dataset using 414 Holstein cattle records collected from 1983 to 2019.Four different feature selection methods were performed to evaluate the essential biological characteristics and feeding-related features which affect milk yield.The results showed that the overall performance of predictive models improved after proper feature selection, with an R 2 value increased to 0.811, and a root mean squared error (RMSE) decreased to 3.627.The stacking model achieved the best performance with an R 2 value of 0.85, a mean absolute error (MAE) of 2.537 and an RMSE of 3.236.This research provides benchmark information for the prediction of milk yield on the CowNflow dataset and identifies useful factors such as dry matter (DM) intake and lactation month in long-term milk yield prediction.

When writing a distributed application, conventional wisdom says to split your application into separate services that can be rolled out independently. This approach is well-intentioned, but a microservices-based architecture like this often backfires, introducing challenges that counteract the benefits the architecture tries to achieve. Fundamentally, this is because microservices conflate logical boundaries (how code is written) with physical boundaries (how code is deployed). In this paper, we propose a different programming methodology that decouples the two in order to solve these challenges. With our approach, developers write their applications as logical monoliths, offload the decisions of how to distribute and run applications to an automated runtime, and deploy applications atomically. Our prototype implementation reduces application latency by up to 15× and reduces cost by up to 9× compared to the status quo.