Approximate product-forms and reversed processes for performance analysis (APROPOS)

用于性能分析的近似产品形式和逆向过程 (APROPOS)

基本信息

批准号：
EP/I030921/1
负责人：
Peter Harrison
金额：
$ 42.68万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2012
资助国家：
英国
起止时间：
2012 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FI030921%2F1
关键词：
Approximate product forms reversed processes

项目摘要

The need for models in the quantitative design of complex computer and communicaton systems is indisputable but their construction is hampered by the lack of a uniform methodology for building large models from smaller component-models - mirroring the design process of the systems themselves. Various specific, often ad hoc, techniques have been developed over the last four decades in response to contemporary design features, beginning with queueing networks that modeled multiprogrammed mainframe systems through to network-calculus descriptions of mobile networks and stochastic models of telecommunication systems and the internet. Unfortunately, such models are prone to exponential (or higher) growth in computational complexity, a phenomenon often referred to as state-space explosion .The present proposal aims to provide techniques and tools for deriving efficient, mainly approximate, solutions to models of modern networks. As in almost all analyses of such complex systems, the aim is to find separable solutions, which allow subsystems (components) to be solved separately and their solutions to then be combined in a simple way. More specifically, we propose to develop the recent significant results obtained by the proposer and Dr Andrea Marin through a three-year project supporting Marin as the RA. The said results have led to a number of research and tutorial papers that have been accepted and/or presented at the highest quality international venues in our research area. It is the proposer's view that the research is at a knee in an upward curve and the opportunity to work directly with a rising-star such as Marin is one not to be missed if at all possible; in addition to the above papers and tutorials, Marin has won best paper awards at two international conferences for his work in this area in the last 12 months. Furthermore, the presentation at Sigmetrics 2010 in New York was well received and led to several discussions with leading international researchers and plans for specific collaboration - see the Case for Support. The theoretical research proposed will supplement existing analytical techniques, such as queueing network modeling (QNM), which are still relevant but are lacking in expressive power for modeling today's systems. For example, Stochastic Petri Nets (SPNs) are suitable for describing virtual resource systems, as used in cloud computing for example, which is not the case for standard queueing networks. Similarly, (non-standard) networks with batch movements are important in models of energy-efficient systems (see the Case for Support), but in general do not have separable (or other efficient) solutions; preliminary results relating to this will be presented as a poster at Performance'10 in November. Last, optimisation is facilitated by the ability of our unique approach, using the Reversed Compound Agent Theorem (RCAT), to perturb specifications so as to create separable solutions, admitting the possibility of searching for a best-fit product-form solution.Based on these theoretical and practical developments, the first objective of the proposed project is to enhance the RCAT-approach to product-forms and semi-product-forms for application in models with SPN specifications and in networks with batch-movements. Perhaps even more importantly, the probability density function of the response time of tasks in passing along a path will be investigated in a new class of networks; preliminary results have already been obtained and will also be presented in the aforementioned poster. This work is a substantial advance on established approaches, such as Boxma and Daduna's as well as that of the proposed PI previously. We believe that three years of uninterrupted collaboration between the proposer and Marin in the AESOP research group will attain the goals summarised above and listed under Objectives .

复杂计算机和通信系统定量设计中模型的需求是无可争议的，但是由于缺乏从较小的组件模式构建大型模型的统一方法所阻碍它们的构造 - 反映了系统本身的设计过程。在过去的四十年中，已经开发了各种特定的，通常是临时的技术，该技术响应当代设计功能，从排队的网络开始，这些网络将多编程的大型机系统建模到对移动网络的网络 - 钙库描述，以及对电信系统和互联网的随机模型的描述。不幸的是，此类模型容易在计算复杂性中成倍增长，这一现象通常称为状态空间爆炸。目前的建议旨在为实现现代网络模型的高效（主要是近似的解决方案）提供技术和工具。与几乎所有对这种复杂系统的分析中一样，目的是找到可分离的解决方案，该解决方案允许子系统（组件）分开求解，然后以简单的方式将其解决方案组合在一起。更具体地说，我们建议通过支持Marin作为RA的三年项目，开发提案者和Andrea Marin博士获得的最新重要结果。所述结果导致了许多研究和教程论文，这些论文已在我们研究领域的最高质量国际场所接受和/或介绍。提议者的观点是，这项研究是在向上的曲线膝盖，并且有机会直接与像马林这样的新兴明星合作，这是不容错过的。除了上述论文和教程外，马林还在过去12个月内在这一领域的工作中获得了两次国际会议上的最佳纸张奖。此外，在纽约Sigmetrics 2010举行的Sigmetrics的演讲得到了很好的接待，并引发了与领先的国际研究人员和特定协作计划的几次讨论 - 请参阅此案以寻求支持。提出的理论研究将补充现有的分析技术，例如排队网络建模（QNM），这些技术仍然相关，但缺乏对当今系统建模的表达能力。例如，随机培养皿网（SPN）适用于描述云计算中使用的虚拟资源系统，例如，标准排队网络不是这种情况。同样，具有批处理移动的（非标准）网络在节能系统模型中很重要（请参阅支持案例），但通常没有可分离（或其他有效的）解决方案；与此有关的初步结果将在11月10日作为Performance'10的海报提出。 Last, optimisation is facilitated by the ability of our unique approach, using the Reversed Compound Agent Theorem (RCAT), to perturb specifications so as to create separable solutions, admitting the possibility of searching for a best-fit product-form solution.Based on these theoretical and practical developments, the first objective of the proposed project is to enhance the RCAT-approach to product-forms and semi-product-forms for application in models with SPN规格和具有批处理的网络。也许更重要的是，任务响应时间的概率密度函数将在新的网络中进行研究。初步结果已经获得，也将在上述海报中提出。这项工作是既定方法，例如Boxma和Daduna以及以前提议的PI的实质性进步。我们认为，伊索研究小组中提议者和马林之间的三年不间断的合作将实现上述总结并在目标下列出的目标。