资源适应型多粒度负载的均衡机制及应用研究
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摘要
数据库作为信息系统中最基础的一个环节,其性能表现成为应用性能关键。同时无论大型或者小型应用系统都存在充分利用服务器资源的需求。而现在的实际状况是服务器的利用率低造成了资源浪费,同时因为资源得不到有效利用又使得系统的服务质量下降,具体的表现就是系统无法对用户的需求进行及时响应。基于以上原因本文通过对数据库负载的静态特征及负载的时间和概率分布特性的研究分析,来更好的规划数据库系统及数据库集群中数据的分布以达到提高系统性能的目的。同时可以根据各个应用系统不同的负载特性,来进行应用系统的规划和部署,以达到充分利用各个服务器资源的目的。这样就能达到软件结构与硬件资源的匹配。
本文在深入分析现在软件性能测试和度量方法的基础上,以数据库负载特征化为主线,结合负载调度和均衡策略,对数据库的负载建模和服务器资源建模进入了深入研究,并设计和实现了能够支持本文提出建模方法的数据库自动测试化的平台。针对数据库负载性能的具体的创新和贡献如下：
●研究了数据库服务器性能指标及资源消耗模式与负载之间的关系。提出了针对负载进行特征化的方法和指标。在此对服务器的性能指标之间的关系进行了量化的相关性研究,摒弃对数据库负载的性能影响不大的参数,减少了服务器建模时需要的性能指标,降低了负载均衡算法的复杂度,提高负载均衡算法的效率。
●研究了数据库服务器基于负载的任务均衡策略和调度方案。提出了基于负载特征信息的动静结合的任务调度策略。该调度结合了负载均衡调度动态和静态调度方案的优势,并且由于准确的特征参数的选择能有效提高负载均衡算法的准确度和响应时间。
●构建和实现了数据层操作特性分析平台。在数据库应用系统中,数据库服务器的容量、数据库结构、存储结构和环境配置直接影响数据层处理任务的性能。依据数据库运行日志和跟踪信息,通过自动关联分析和转换处理,按照负载时空特性或特殊配置要求生成数据层任务处理脚本,该脚本可以在数据库平台上重复执行,并以此获取数据库服务器数据在处理不同类型的任务是所对应的资源消耗模式和服务器针对不同类型的任务的处理性能。
●根据负载信息的特征性建模,在数据层操作特性分析平台上利用分层排队网络建模技术对数据库服务器建立性能模型,管理服务器CPU、磁盘、查询代理进程、数据管理进程等软件和硬件资源。
以上的研究可以为数据库服务器容量规划、存储结构设计、关键功能设计和性能优化提供依据,也可为云计算平台实现资源和计算能力按照需求优化配置提供依据。提高服务器利用率并达到服务集群单位时间的最大吞吐量。
As the most basic aspect of application system, DBMS's performances become critical application performance. Whether large or small applications both systems exist to fully utilize server resources. And now the actual situation is the server's utilization rate resulted in a waste of resources, but because of lack of resources but also makes effective use of the system's service quality degradation, the specific performance of the system is unable to respond to the needs of users in a timely manner. For these reasons this paper, the static characteristics of the database load and load characteristics of the probability distribution of the time and research and analysis to better planning database systems and database cluster in the distribution of data in order to achieve the purpose of improving system performance. While different according to each application system load characteristics for the planning and deployment of application systems in order to make full use of each server resources. This achieves the software architecture and hardware resources of the match.
In-depth analysis of software performance testing and measurement methods based on the database workload characteristics into the main line, combined load scheduling and balanced strategy to enter the in-depth study of load modeling and database server resources modeling, design and realization In this paper, the modeling method can support the database automatically test platform. Specific innovation for database workload performance and contribution are as follows:
●The study the relationship between the database server performance and resource consumption patterns and load. Methods and indicators for load characterization. Quantitative correlation study of the relationship between the performance of the server in this abandoned little impact on the performance of the database load parameters, reducing the the server modeling of performance indicators, reduce the load balancing algorithm complexity and improve the efficiency of the load balancing algorithm.
●Database server tasks based on the load balancing strategy and scheduling scheme. Task scheduling strategy based on the combination of the movement of the load characteristic information. The scheduler combines the advantages of the load balancing, scheduling dynamic and static scheduling scheme, and accurately select the characteristic parameters can improve the accuracy of the load balancing algorithm and the corresponding times.
●Builded and realized the operating characteristics of the data layer analysis platform. Database application system, the capacity of the database server, database structure, and storage structure and environment configuration directly affect the performance of the data layer processing tasks. By auto-correlation analysis and conversion process in accordance with the database run log and trace information, in accordance with the load spatial and temporal characteristics or special configuration requirements generate data layer tasking. Script, the script can be executed repeatedly database platform and access to the database server data processing different types of tasks are the resource consumption of the corresponding mode and the processing performance of the server for the different types of tasks.
●According to the load characteristic information modeling, using layered queuing network modeling techniques in the operating characteristics of the data layer analysis platform to create a performance model of the database server, the software and hardware of the management server CPU, disk, query agent process, data management process resources.
The above study provide the basis for cloud computing platform to achieve optimal allocation of resources and computing power in accordance with the demand to provide a basis for capacity planning of the database server, the storage structure design, the key features of the design and performance optimization. Improve server utilization and achieve the maximum throughput of the service cluster unit time.
本文在深入分析现在软件性能测试和度量方法的基础上,以数据库负载特征化为主线,结合负载调度和均衡策略,对数据库的负载建模和服务器资源建模进入了深入研究,并设计和实现了能够支持本文提出建模方法的数据库自动测试化的平台。针对数据库负载性能的具体的创新和贡献如下：
●研究了数据库服务器性能指标及资源消耗模式与负载之间的关系。提出了针对负载进行特征化的方法和指标。在此对服务器的性能指标之间的关系进行了量化的相关性研究,摒弃对数据库负载的性能影响不大的参数,减少了服务器建模时需要的性能指标,降低了负载均衡算法的复杂度,提高负载均衡算法的效率。
●研究了数据库服务器基于负载的任务均衡策略和调度方案。提出了基于负载特征信息的动静结合的任务调度策略。该调度结合了负载均衡调度动态和静态调度方案的优势,并且由于准确的特征参数的选择能有效提高负载均衡算法的准确度和响应时间。
●构建和实现了数据层操作特性分析平台。在数据库应用系统中,数据库服务器的容量、数据库结构、存储结构和环境配置直接影响数据层处理任务的性能。依据数据库运行日志和跟踪信息,通过自动关联分析和转换处理,按照负载时空特性或特殊配置要求生成数据层任务处理脚本,该脚本可以在数据库平台上重复执行,并以此获取数据库服务器数据在处理不同类型的任务是所对应的资源消耗模式和服务器针对不同类型的任务的处理性能。
●根据负载信息的特征性建模,在数据层操作特性分析平台上利用分层排队网络建模技术对数据库服务器建立性能模型,管理服务器CPU、磁盘、查询代理进程、数据管理进程等软件和硬件资源。
以上的研究可以为数据库服务器容量规划、存储结构设计、关键功能设计和性能优化提供依据,也可为云计算平台实现资源和计算能力按照需求优化配置提供依据。提高服务器利用率并达到服务集群单位时间的最大吞吐量。
As the most basic aspect of application system, DBMS's performances become critical application performance. Whether large or small applications both systems exist to fully utilize server resources. And now the actual situation is the server's utilization rate resulted in a waste of resources, but because of lack of resources but also makes effective use of the system's service quality degradation, the specific performance of the system is unable to respond to the needs of users in a timely manner. For these reasons this paper, the static characteristics of the database load and load characteristics of the probability distribution of the time and research and analysis to better planning database systems and database cluster in the distribution of data in order to achieve the purpose of improving system performance. While different according to each application system load characteristics for the planning and deployment of application systems in order to make full use of each server resources. This achieves the software architecture and hardware resources of the match.
In-depth analysis of software performance testing and measurement methods based on the database workload characteristics into the main line, combined load scheduling and balanced strategy to enter the in-depth study of load modeling and database server resources modeling, design and realization In this paper, the modeling method can support the database automatically test platform. Specific innovation for database workload performance and contribution are as follows:
●The study the relationship between the database server performance and resource consumption patterns and load. Methods and indicators for load characterization. Quantitative correlation study of the relationship between the performance of the server in this abandoned little impact on the performance of the database load parameters, reducing the the server modeling of performance indicators, reduce the load balancing algorithm complexity and improve the efficiency of the load balancing algorithm.
●Database server tasks based on the load balancing strategy and scheduling scheme. Task scheduling strategy based on the combination of the movement of the load characteristic information. The scheduler combines the advantages of the load balancing, scheduling dynamic and static scheduling scheme, and accurately select the characteristic parameters can improve the accuracy of the load balancing algorithm and the corresponding times.
●Builded and realized the operating characteristics of the data layer analysis platform. Database application system, the capacity of the database server, database structure, and storage structure and environment configuration directly affect the performance of the data layer processing tasks. By auto-correlation analysis and conversion process in accordance with the database run log and trace information, in accordance with the load spatial and temporal characteristics or special configuration requirements generate data layer tasking. Script, the script can be executed repeatedly database platform and access to the database server data processing different types of tasks are the resource consumption of the corresponding mode and the processing performance of the server for the different types of tasks.
●According to the load characteristic information modeling, using layered queuing network modeling techniques in the operating characteristics of the data layer analysis platform to create a performance model of the database server, the software and hardware of the management server CPU, disk, query agent process, data management process resources.
The above study provide the basis for cloud computing platform to achieve optimal allocation of resources and computing power in accordance with the demand to provide a basis for capacity planning of the database server, the storage structure design, the key features of the design and performance optimization. Improve server utilization and achieve the maximum throughput of the service cluster unit time.
引文
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