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PRISMA: Efficient Algorithms and Methods for Online Extraction of Performance Models in Virtualized Environments

PRISMA：虚拟化环境中在线提取性能模型的高效算法和方法

基本信息

批准号：
251959028
负责人：
Professor Dr.-Ing. Samuel Kounev
金额：
--
依托单位：
Lehrstuhl für Informatik II (Software Engineering)
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2015
资助国家：
德国
起止时间：
2014-12-31 至 2021-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/251959028?language=en
关键词：
PRISMA Efficient Algorithms Methods Online

项目摘要

Modern software systems are becoming increasingly complex and dynamic. Systems typically have a layered architecture which includes application components, middleware platform(s), virtual machine(s), hypervisor, and physical hardware. Each layer influences the system performance, however, it is a major challenge to separate, isolate and quantify the performance influences of each layer while explicitly taking into account configuration and deployment parameters which can change dynamically during operation. Over the past five years, layered architecture-level performance models have become increasingly popular as a powerful tool for run-time performance management, however, such models are usually costly to build manually and require extensive experimental analysis in a controlled environment. The aim of the PRISMA project is to develop novel methods for automatic extraction of architecture-level performance models of virtualization platforms and their hosted applications during system operation. The project will provide a novel reference architecture for virtualization platforms and virtual appliances with integrated model extraction capabilities. The extraction will be based solely on generic model skeletons (composable model building-blocks) integrated into the platforms at the virtualization and middleware level, and monitoring data collected at system run-time, without assuming availability of source code or possibility to conduct static code analysis. The extracted performance models will capture the performance-relevant aspects of the hosted applications and their execution environment including the virtualization platform itself. Thus, extensive and costly experimental analysis to build performance models for capacity management will no longer be necessary. The developed methods will facilitate the creation of performance models and will lay the foundation for proactive performance and resource management by means of the models. By automating and integrating online model extraction, refinement and maintenance as features provided by the virtualization platform, PRISMA will set the basis for a breakthrough in the practical use of predictive models in real-life systems. The adoption of model-based run-time management techniques promises to significantly improve the efficiency of modern virtualized service infrastructures by avoiding to over-provision system resources without having to sacrifice application performance guarantees.

现代软件系统正变得越来越复杂和动态。系统通常具有分层架构，其包括应用组件、中间件平台、虚拟机、管理程序和物理硬件。每一层都影响系统性能，然而，在明确考虑在操作期间可能动态变化的配置和部署参数的同时，分离、隔离和量化每一层的性能影响是一个主要挑战。在过去的五年中，分层的体系结构级的性能模型已经成为运行时性能管理的一个强大的工具越来越受欢迎，然而，这样的模型通常是昂贵的手动构建，并需要在受控环境中进行大量的实验分析。PRISMA项目的目的是开发新的方法，用于在系统运行期间自动提取虚拟化平台及其托管应用程序的架构级性能模型。该项目将为虚拟化平台和具有集成模型提取功能的虚拟设备提供一种新颖的参考架构。提取将仅基于在虚拟化和中间件级别集成到平台中的通用模型框架（可组合模型构建块），并监控在系统运行时收集的数据，而不假设源代码的可用性或进行静态代码分析的可能性。提取的性能模型将捕获托管应用程序及其执行环境（包括虚拟化平台本身）的性能相关方面。因此，将不再需要为容量管理建立性能模型而进行广泛和昂贵的实验分析。所开发的方法将有助于创建性能模型，并将奠定基础，积极主动的性能和资源管理的模型。通过自动化和集成在线模型提取、细化和维护作为虚拟化平台提供的功能，PRISMA将为在现实系统中实际使用预测模型奠定基础。采用基于模型的运行时管理技术，通过避免过度配置系统资源而不必牺牲应用程序性能保证，有望显着提高现代虚拟化服务基础设施的效率。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Modeling of Parametric Dependencies for Performance Prediction of Component-Based Software Systems at Run-Time

基于组件的软件系统运行时性能预测的参数依赖性建模

DOI：
10.1109/icsa.2018.00023
发表时间：
2018
期刊：
2018 IEEE International Conference on Software Architecture (ICSA)
影响因子：
0
作者：
Simon Eismann;Jürgen Walter;Jóakim von Kistowski;Samuel Kounev
通讯作者：
Samuel Kounev

Online Learning of Run-Time Models for Performance and Resource Management in Data Centers

数据中心性能和资源管理运行时模型的在线学习

DOI：
10.1007/978-3-319-47474-8_17
发表时间：
2017
期刊：
影响因子：
0
作者：
Jürgen Walter;Antinisca Di Marco;Simon Spinner;Paola Inverardi;Samuel Kounev
通讯作者：
Samuel Kounev

Online model learning for self-aware computing infrastructures

自我意识计算基础设施的在线模型学习

DOI：
10.1016/j.jss.2018.09.089
发表时间：
2019
期刊：
J. Syst. Softw.
影响因子：
0
作者：
Simon Spinner;Johannes Grohmann;Simon Eismann;Samuel Kounev
通讯作者：
Samuel Kounev

Detecting Parametric Dependencies for Performance Models Using Feature Selection Techniques

使用特征选择技术检测性能模型的参数依赖性

DOI：
10.1109/mascots.2019.00042
发表时间：
2019
期刊：
2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS)
影响因子：
0
作者：
Johannes Grohmann;Simon Eismann;Sven Elﬂein;Manar Mazkatli;Jóakim von Kistowski;Samuel Kounev
通讯作者：
Samuel Kounev

Self-Tuning Resource Demand Estimation