基于D3QN的Spark Streaming的创新参数优化,使用高斯过程回归
Hong Zhang1, Zhenchao Xu1, Yunxiang Wang1
1School of Cyber Security and Computer, Hebei University, Baoding, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
本研究介绍了一种改进的双重深度Q网络 (DQN) 决斗,以优化Spark流媒体性能. 这种新的方法通过自动化参数调整来显著提高数据处理效率,达到高达30.24%的改进.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 大数据分析大数据分析
背景情况:
- 闪电流对于处理来自社交媒体和物联网设备等来源的实时数据至关重要.
- 优化Spark Streaming的性能至关重要,因为它在数据分析中的广泛使用.
- 用于Spark Streaming的手动参数调整是复杂和低效的,涉及200多个参数.
研究的目的:
- 开发一种自动化和高效的方法来优化Spark流媒体性能.
- 为了应对Spark Streaming中手动参数配置的挑战.
- 通过智能参数调节,显著提高Spark Streaming的性能.
主要方法:
- 提出了一种改进的决斗双深Q网络 (DQN) 技术,用于自动参数调节.
- 集成的强化学习与高斯过程回归加速融合.
- 专注于优化任务调度,资源分配和Spark Streaming中的数据偏差.
主要成果:
- 拟议的对决双DQN方法与高斯过程回归表明了显著的性能改进.
- 在Spark流媒体性能方面实现了高达30.24%的增强.
- 减少了参数调整所需的代次数,并加快了融合速度.
结论:
- 改进的决斗双DQN技术为Spark Streaming性能优化提供了非常有效的解决方案.
- 使用强化学习和高斯过程回归的自动参数调整比手工方法更有效.
- 这种方法为大数据流处理提供了一个可扩展和强大的工具.
相关概念视频
01:20Maxwell-Boltzmann Distribution: Problem Solving
1.6K
Individual molecules in a gas move in random directions, but a gas containing numerous molecules has a predictable distribution of molecular speeds, which is known as the Maxwell-Boltzmann distribution, f(v).
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
1.6K
01:26Distributions to Estimate Population Parameter
4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
01:06Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
96
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
96
01:10Gauss's Law: Problem-Solving
1.8K
Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area...
1.8K
01:52Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
01:20Cluster Sampling Method
12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.0K