Complex interacting networks and systems: Models, analysis, and algorithms

复杂的交互网络和系统：模型、分析和算法

• 批准号: RGPIN-2015-05218
• 负责人: Mazumdar, Ravi
• 金额: $ 2.19万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683396
• 关键词: Complex; interacting; networks; systems; Models

Complex systems and networks such as the internet, wireless networks, the cloud, and social networks, are comprised of large numbers of interacting entities (users and resources) that interact in a random way that determine their functionality and the macroscopic behavior of the overall system. To manage the complexity of networks and systems we need to understand their dynamic as well as emergent behavior in order to design algorithms for their stable, efficient, and secure operation as well as for providing user experience. The systems of today are vastly different in scale and the basic issues are not simply traffic issues. The methodologies required are therefore very different that go beyond classical queueing models, optimization,  and  resource allocation. Since the systems are large in terms of users, resources, and are spatially distributed we need to understand the issues of local vs. global behavior and the design of distributed algorithms that do not require too much information. The research will aim at understanding the impact of heterogeneity and the size and population of resources and how these can be used to characterize the performance. ***The  research will concern itself with the issues of complexity and the stochastic behavior of such networks and systems. The aim is to address the main problems that impact stability and performance. The goal is the design of  simple efficient and distributed algorithms for user experience and system efficiency. The techniques will draw upon stochastic networks, generalized voter models, mean field or McKean-Vlasov analysis, randomized algorithms, random graphs especially the spectral theory, and bandit processes.***The research will be pursued with the following principal application thrusts: 1) Wireless networks, 2) Social networks and security, 3) Network performance and algorithms , and 4) Cloud computing and applications.  The aim is to study the interplay between size and emergent behavior, while using averaging to devise  simple algorithms for resource optimization and providing predictable performance to both users and operators.***In each of the constituent application areas the research contributions will be in models, their analysis with performance being the focus, and the design of distributed and randomized algorithms for scalability without sacrificing efficiency. The aim is to develop the theoretical frameworks necessary for the design of robust and stable algorithms that will allow resource allocation and optimization. A key component is the training research students to acquire wider variety of tools and techniques that are necessary to deal with the new problems that are not part of classical training which will lead to new courses and monographs. The research likely to enrich the constituent fields from where the tools will be drawn namely stochastic networks, bandit processes, randomized algorithms and graphical models.

复杂的系统和网络，如互联网、无线网络、云和社交网络，由大量的交互实体（用户和资源）组成，这些实体以随机的方式交互，决定了它们的功能和整个系统的宏观行为。为了管理网络和系统的复杂性，我们需要了解它们的动态和紧急行为，以便设计算法来实现稳定，高效和安全的操作以及提供用户体验。今天的系统在规模上有很大的不同，基本问题不仅仅是交通问题。因此，所需的方法是非常不同的，超越了经典的生产模型，优化和资源分配。由于系统在用户、资源和空间分布方面都很大，我们需要了解局部与全局行为的问题，以及不需要太多信息的分布式算法的设计。该研究旨在了解异质性的影响以及资源的规模和数量，以及如何使用这些来表征性能。* 本研究将关注此类网络和系统的复杂性和随机行为问题。其目的是解决影响稳定性和性能的主要问题。我们的目标是设计简单高效的分布式算法，以提高用户体验和系统效率。这些技术将利用随机网络，广义选民模型，平均场或McKean-Vlasov分析，随机算法，随机图，特别是谱理论，以及强盗过程。研究将以下列主要应用为重点：1）无线网络，2）社交网络和安全，3）网络性能和算法，4）云计算和应用。目的是研究规模和紧急行为之间的相互作用，同时使用平均来设计简单的算法，用于资源优化，并为用户和运营商提供可预测的性能。*在每个组成的应用领域的研究贡献将在模型，他们的分析与性能是重点，并在不牺牲效率的可扩展性的分布式和随机算法的设计。其目的是开发必要的理论框架，设计强大和稳定的算法，将允许资源分配和优化。一个关键组成部分是培训研究生获得更广泛的工具和技术，这些工具和技术是处理不属于经典培训的新问题所必需的，这将导致新的课程和专著。这项研究可能会丰富组成领域的工具，即随机网络，强盗过程，随机算法和图形模型。