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

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms

Degradation kinetic study of lysine in lysine hydrochloride solutions for injection by determining its main degradation product

AbstractA limited number of researches have been reported to apply the Arrhenius equation to study the relationship between drugs and its degradation products so far. In the present work, the thermal degradation kinetics of lysine hydrochloride solutions for injection, the special solvent for ademetionine 1, 4-butanedisulfonate (SAM) for injection, was investigated at selected temperatures and pH values. The main degradation product of lysine was separated, purified, and confirmed as lysine lactam. A reversed-phase high performance liquid chromatographic (RP-HPLC) method without derivation was developed for the simultaneous determination of lysine and lysine lactam. The results confirmed that both the lysine degradation and lysine lactam generation followed zero-order reaction kinetics. The degradation and generation rate constants increased with increasing temperatures and decreasing pH values. The temperature-dependent degradation and generation reaction could be sufficiently modeled on the Arrhenius equation with the activation energy of 80.14 and 83.22 kJ/mol, respectively. Meanwhile, a linear relationship existed between the amount of lysine degradation and lysine lactam generation since the approximate activation energy. Considering there could be other side effects, we established an upper limit of lysine lactam (500 μg/ml), as the acceptable criteria for stability to estimate the shelf life together with lysine, which made the prediction more accurate and credible. Extrapolation data demonstrated that the lysine hydrochloride solutions for injection could be stable for two years stored at room temperature

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