ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees

To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a MySQL deployment with well-tuned configuration parameters achieves a peak throughput as 12 times much as one with the default setting. However, finding the best setting for the tens or hundreds of configuration parameters is mission impossible for ordinary users. Worse still, many Big Data applications require the support of multiple systems co-deployed in the same cluster. As these co-deployed systems can interact to affect the overall performance, they must be tuned together. Automatic configuration tuning with scalability guarantees (ACTS) is in need to help system users. Solutions to ACTS must scale to various systems, workloads, deployments, parameters and resource limits. Proposing and implementing an ACTS solution, we demonstrate that ACTS can benefit users not only in improving system performance and resource utilization, but also in saving costs and enabling fairer benchmarking

BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning

An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped the performance potential of systems. A good configuration setting can greatly improve the performance of a deployed system under certain workloads. But with tens or hundreds of parameters, it becomes a highly costly task to decide which configuration setting leads to the best performance. While such task requires the strong expertise in both the system and the application, users commonly lack such expertise. To help users tap the performance potential of systems, we present BestConfig, a system for automatically finding a best configuration setting within a resource limit for a deployed system under a given application workload. BestConfig is designed with an extensible architecture to automate the configuration tuning for general systems. To tune system configurations within a resource limit, we propose the divide-and-diverge sampling method and the recursive bound-and-search algorithm. BestConfig can improve the throughput of Tomcat by 75%, that of Cassandra by 63%, that of MySQL by 430%, and reduce the running time of Hive join job by about 50% and that of Spark join job by about 80%, solely by configuration adjustment

Epigenetic control of embryo–uterine crosstalk at peri-implantation

Abstract(#br)Embryo implantation is one of the pivotal steps during mammalian pregnancy, since the quality of embryo implantation determines the outcome of ongoing pregnancy and fetal development. A large number of factors, including transcription factors, signalling transduction components, and lipids, have been shown to be indispensable for embryo implantation. Increasing evidence also suggests the important roles of epigenetic factors in this critical event. This review focuses on recent findings about the involvement of epigenetic regulators during embryo implantation

A Chaotic Elite Niche Evolutionary Algorithm for Low-Power Clustering in Environment Monitoring Wireless Sensor Networks

In recent years, as people’s demand for environmental quality has increased, it has become inevitable to monitor sensitive parameters such as temperature and oxygen content. Environmental monitoring wireless sensor networks (EMWSNs) have become a research hotspot because of their flexibility and high monitoring accuracy. This paper proposes a chaotic elite niche evolutionary algorithm (CENEA) for low-power clustering in EMWSNs. To verify the performance of CENEA, simulation experiments are carried out in this paper. Through simulation experiments, CENEA was compared with shuffled frog leaping algorithm (SFLA), differential evolution algorithm (DE), and genetic algorithm (GA) in the same conditional parameters. The results show that CENEA balances node energy and improved node energy usage efficiency. CENEA’s network energy consumption is reduced by 8.3% compared to SFLA, 3.9% lower than DE, and 4.6% lower than GA. Moreover, CENEA improves the precision and minimizes the computation time

A Combined Model of SARIMA and Prophet Models in Forecasting AIDS Incidence in Henan Province, China

Acquired immune deficiency syndrome (AIDS) is a serious public health problem. This study aims to establish a combined model of seasonal autoregressive integrated moving average (SARIMA) and Prophet models based on an L1-norm to predict the incidence of AIDS in Henan province, China. The monthly incidences of AIDS in Henan province from 2012 to 2020 were obtained from the Health Commission of Henan Province. A SARIMA model, a Prophet model, and two combined models were adopted to fit the monthly incidence of AIDS using the data from January 2012 to December 2019. The data from January 2020 to December 2020 was used to verify. The mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used to compare the prediction effect among the models. The results showed that the monthly incidence fluctuated from 0.05 to 0.50 per 100,000 individuals, and the monthly incidence of AIDS had a certain periodicity in Henan province. In addition, the prediction effect of the Prophet model was better than SARIMA model, the combined model was better than the single models, and the combined model based on the L1-norm had the best effect values (MSE = 0.0056, MAE = 0.0553, MAPE = 43.5337). This indicated that, compared with the L2-norm, the L1-norm improved the prediction accuracy of the combined model. The combined model of SARIMA and Prophet based on the L1-norm is a suitable method to predict the incidence of AIDS in Henan. Our findings can provide theoretical evidence for the government to formulate policies regarding AIDS prevention

Overexpression of a Gene Encoding Trigonelline Synthase from Areca catechu L. Promotes Drought Resilience in Transgenic Arabidopsis

Areca catechu L. is a commercially important palm tree widely cultured in tropical and subtropical areas. Its growth and production are severely hindered by the increasing threat of drought. In the present study, we investigated the physiological responses of areca seedlings to drought stress. The results showed that prolonged drought-induced yellowing on the overall area of most leaves significantly altered the chlorophyll fluorescence parameters, including maximum chemical efficiency (Fv/Fm), photochemical efficiency of PSII (Y(II)), photochemical chlorophyll fluorescence quenching (qP) and non-photochemical chlorophyll fluorescence quenching (NPQ). On the 10th day of drought treatment, the contents of proline in the areca leaves and roots increased, respectively, by 12.2 times and 8.4 times compared to normal watering. The trigonelline levels in the leaves rose from 695.35 µg/g to 1125.21 µg/g under 10 days of water shortage, while no significant changes were detected in the content of trigonelline in the roots. We determined the gene encoding areca trigonelline synthase (AcTS) by conducting a bioinformatic search of the areca genome database. Sequence analysis revealed that AcTS is highly homologous to the trigonelline synthases in Coffea arabica (CaTS 1 and CaTS 2) and all possess a conserved S-adenosyl- L-methionine binding motif. The overexpression of AcTS in Arabidopsis thaliana demonstrated that AcTS is responsible for the generation of trigonelline in transgenic Arabidopsis, which in turn improves the drought resilience of transgenic Arabidopsis. This finding enriches our understanding of the molecular regulatory mechanism of the response of areca to water shortage and provides a foundation for improving the drought tolerance of areca seedlings