Life-cycle management for network slices

网络切片的生命周期管理

• 批准号: RGPIN-2021-04176
• 负责人: Shahriar, Nashid
• 金额: $ 2.11万
• 依托单位: University of Regina
• 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=749908
• 关键词: Life; cycle; management; network; slices

Network slicing is an enabling technology for the future Internet and next-generation communication networks (5G and beyond 5G mobile networks). It transforms a network from a "one size fits all" architecture, to a new paradigm where network slices/virtual networks are created, with appropriate isolation, resources, and optimized topology to serve a particular application or service. Life-cycle management of network slices is important to meet applications' Quality of Service (QoS) requirements on a dynamic and evolving network. Slice life-cycle management involves three phases: i) instantiation, configuration, and activation phase to orchestrate slices, with diverse QoS requirements, possibly spanning multiple network segments; ii) run-time phase to perform slice operational and maintenance tasks such as, performance monitoring, failure management, and resource adaptation; and iii) decommissioning phase that re-optimizes resources after decommissioning network slice(s). Slice life-cycle management faces unique challenges due to incorporation of virtualization and software defined networking, and traditional management approaches fall short in ensuring best slice operational performance, desired resource efficiency, and rapid incident management. This research program aims at overcoming some of the challenges pertaining to the development of a life-cycle management system for 5G and beyond 5G network slices. Towards this goal, my students and I will pursue three main objectives. First, we will develop optimization models and scalable algorithms for instantiating slices with heterogeneous QoS requirements to minimize capital and operational expenses. Second, we will explore optimization and data-driven techniques to devise intelligent monitoring schemes, proactive and reactive failure management approaches, and predictive slice adaptation mechanisms. Failure management approaches will include machine learning based anomaly detection and mitigation as well as automated failure detection, localization, and identification models. Slice adaptation will benefit from predicting a set of slice QoS requirement changes and accommodating them exploiting statistical multiplexing gains. Third, we will devise re-optimization and consolidation algorithms to increase resource efficiency of the network infrastructure. We will evaluate our algorithms and models through empirical and testbed experiments, and validate them using real network data. The outcome of the research program will contribute to the development of a slice life-cycle management system and help Canadian network operators optimize resource utilization, reduce capital and operational expenditures, minimize number of network outages, and increase revenue. My students will develop optimization frameworks, algorithmic tools, and machine learning models and evaluate them through rigorous experiments, and accumulating skills along the way that will serve them well in industry or academia.

网络切片是未来互联网和下一代通信网络（5G及5G以后的移动的网络）的使能技术。它将网络从“一刀切”的架构转变为新的范例，在新的范例中创建网络切片/虚拟网络，并具有适当的隔离、资源和优化的拓扑结构，以服务于特定的应用程序或服务。网络切片的生命周期管理对于满足动态和不断发展的网络上的应用的服务质量（QoS）要求非常重要。切片生命周期管理涉及三个阶段：i）实例化、配置和激活阶段，以协调切片，具有不同的QoS要求，可能跨越多个网段; ii）运行时阶段，执行切片操作和维护任务，例如性能监控、故障管理和资源适配;以及iii）退役阶段，在退役网络切片后重新优化资源。由于虚拟化和软件定义网络的结合，切片生命周期管理面临着独特的挑战，传统的管理方法无法确保最佳切片运营性能、所需的资源效率和快速的事件管理。该研究计划旨在克服与5G及5G以上网络切片的生命周期管理系统开发有关的一些挑战。为了实现这一目标，我和我的学生将追求三个主要目标。首先，我们将开发优化模型和可扩展的算法，用于实例化具有异构QoS要求的切片，以最大限度地减少资本和运营费用。其次，我们将探索优化和数据驱动技术，以设计智能监控方案，主动和被动故障管理方法以及预测切片自适应机制。故障管理方法将包括基于机器学习的异常检测和缓解，以及自动故障检测、定位和识别模型。切片自适应将受益于预测一组切片QoS要求的变化，并利用统计复用增益来容纳它们。第三，我们将设计重新优化和整合算法，以提高网络基础设施的资源效率。我们将通过经验和测试平台实验来评估我们的算法和模型，并使用真实的网络数据来验证它们。该研究计划的成果将有助于开发切片生命周期管理系统，并帮助加拿大网络运营商优化资源利用率，减少资本和运营支出，最大限度地减少网络中断次数，并增加收入。我的学生将开发优化框架，算法工具和机器学习模型，并通过严格的实验对其进行评估，并沿着将在工业或学术界为他们服务的方式积累技能。