Modelling, Performance, and Decision Making of Queues for Wireless Networks and Beyond

无线网络及其他网络的队列建模、性能和决策

• 批准号: RGPIN-2022-04915
• 负责人: Zhao, Yiqiang
• 金额: $ 3.79万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759252
• 关键词: Modelling; Performance; Decision; Making; Queues

In recent years, the proliferation of technologies, easier access to big data, wide introduction of cloud storage and computing, and exponential growth of the Internet, wireless and smartphone apps have profoundly impacted everybody's daily life. These advancements and their subsequent impacts have created many new challenges, which in turn greatly motivate research on large-scale complex systems, multi-dimensional networks, adaptive models, parallel processing, and cloud computing. Many of the new challenges cannot be satisfactorily addressed by classical or standard methods, which call for new innovative mathematical/statistical modeling and solution methodologies. Queueing theory is such an example. Well-developed classical queueing theory and its approaches are not sufficient to meet these modern challenges. This insufficiency results primarily from the large scale and complexity of the system, such as multi-dimensional state spaces, strong dependence in system variables, heterogeneous traffic, non-Poisson behaviour, or incomplete information about the system. The main objective of this proposed research program is to address fundamental issues for the above-mentioned challenges. It aims to develop or extend advanced modeling tools, discover or reveal structure properties, prove theorems, fill the gap between theoretical research and applications, and propose efficient approximations, computations or simulation, and inferences for decision-making. Explicit solutions are not, in general, available for the complex systems proposed in this research. Therefore, approximations, asymptotic properties, performance bounds, and efficient simulation algorithms, computational solutions or statistical inference become key components in the study. We expect that original and significant contributions will be made for the proposed research, in advancing knowledge in the fields of queueing theory, stochastic modelling and analysis, performance evaluation, decision-making, applied probability and operations research. The far-reaching applications of these topics will extend to computer management, telecommunication systems, transportation, manufacturing, health, business and finance, social service systems, and possibly many more. Our constructive HQP training plan, including promotions to recruit more HQP from under-represented groups and improvement of the training environment, will better prepare the trained HQP for future career challenges and making their significant contributions to Canadian economy and society.

近年来，科技进步日新月异，大数据日益普及，云存储和云计算广泛普及，互联网、无线和智能手机应用呈指数级增长，深刻影响着人们的日常生活。这些进步及其后续影响带来了许多新的挑战，这反过来又极大地激发了对大规模复杂系统、多维网络、自适应模型、并行处理和云计算的研究。许多新的挑战不能通过经典或标准方法令人满意地解决，这需要新的创新数学/统计建模和解决方法。排队理论就是这样一个例子。发展良好的经典排队理论及其方法已不足以应对这些现代挑战。这种不足主要是由于系统的大规模和复杂性，如多维状态空间、系统变量的强依赖性、异构流量、非泊松行为或关于系统的不完整信息。这项研究计划的主要目的是解决上述挑战的基本问题。它旨在开发或扩展先进的建模工具，发现或揭示结构属性，证明定理，填补理论研究与应用之间的空白，并提出有效的近似，计算或模拟，以及决策推理。一般来说，对于本研究中提出的复杂系统，显式解是不可用的。因此，近似、渐近性质、性能界限以及高效的仿真算法、计算解或统计推断成为研究的关键组成部分。我们期望在队列理论、随机建模与分析、绩效评估、决策制定、应用概率论和运筹学等领域的研究中做出原创性和重大贡献。这些主题的深远应用将扩展到计算机管理、电信系统、运输、制造、卫生、商业和金融、社会服务系统，可能还有更多。我们建设性的HQP培训计划，包括从代表性不足的群体中招募更多的HQP，改善培训环境，将使培训后的HQP更好地为未来的职业挑战做好准备，并为加拿大经济和社会做出重大贡献。