Intelligent Management Platform - The Future of Cloud and Networking

智能管理平台——云和网络的未来

• 批准号: RGPIN-2021-03626
• 负责人: Jammal, Manar
• 金额: $ 1.75万
• 依托单位: York 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=757250
• 关键词: Intelligent; Management; Platform; Future; Cloud

The complexity of distributed data services is increasing rapidly, especially with the development of mobile computing and the Internet of Things (IoT) where the data volume is exploding. New technologies; Network Functions Virtualization (NFV), 5G Networks, Software-Defined Networking (SDN), Machine Learning (ML), Edge Computing, and Artificial Intelligence (AI); will jeopardize new data silos and require a solution that makes the network and computing simpler. Therefore, the key to business success is the immediate response to user demands and very low time-to-market. My research will aim at taking full advantage of ML algorithms and AI to empower the Cloud, Edge Computing, and NFV/SDN with sophisticated real-time and historical analytics, comprehensive real-time operations, and automation capabilities. My research will build a distributed intelligent computing orchestration framework for NFV components and SDN controllers for IoT applications. The main objective of this proposed work is automating the NFV and SDN orchestration and dynamically enhancing their performance through predicting/detecting the quality of service (QoS)-aware issues (i.e. latency/security-aware applications), automated resource management, and predicting/balancing workloads in real-time across the network infrastructure. It will also focus on using ML algorithms to manage NFV components and SDN controllers while facilitating elasticity (scaling up/down) across the cloud-edge model and handle the increase in users' traffic while maintaining scalability, security, and resiliency. This proposal will start by investigating the network entities' functionalities and how microservices architecture is mapped to NFV and SDN architectures. It will then aim at developing an intelligent cloud-based orchestration approaches to first, facilitate an effective design and management of large-scale NFV applications and SDN controllers and secondly, minimize the work of the information technology team to keep up with the service level agreements and the needs of the rapidly changing technologies and business model. This proposal will develop ML models to automate the choice between cloud and edge deployments especially for mission-critical applications (i.e. health-aware applications) of these technologies. This research will explore analytics for proximity detection, network awareness, self-discovery of resources, and execution of patterns to increase the self-sufficiency of the IoT systems. It will also use predictive analytics to manage resources based on predicted performance and security needs/alarms, dynamically update policies and rules based on real-time traffic characteristics. This research proposal will be based on open source technologies and will integrate the team's practical and technical expertise in networking, cloud computing, distributed systems, ML, data mining and collection, application and data modeling, visualization techniques, and management capabilities.

分布式数据服务的复杂性正在迅速增加，尤其是随着移动计算和物联网（IoT）（IoT）的开发，数据量正在爆炸。新技术；网络功能虚拟化（NFV），5G网络，软件定义的网络（SDN），机器学习（ML），边缘计算和人工智能（AI）；将危害新的数据孤岛，并需要一种解决方案，以使网络和计算更简单。因此，业务成功的关键是对用户需求的立即响应和非常低的市场时间。 我的研究旨在充分利用ML算法和AI，以增强云，边缘计算和NFV/SDN的能力，并具有复杂的实时和历史分析，全面的实时操作以及自动化功能。我的研究将为NFV组件和SDN控制器建立一个分布式的智能计算编排框架，用于物联网应用程序。 The main objective of this proposed work is automating the NFV and SDN orchestration and dynamically enhancing their performance through predicting/detecting the quality of service (QoS)-aware issues (i.e. l​​atency/security-aware applications), automated resource management, and predicting/整个网络基础架构实时平衡工作负载。它还将重点放在使用ML算法来管理NFV组件和SDN控制器的同时，同时促进云层模型上的弹性（向上/下），并处理用户流量的增加，同时保持可扩展性，安全性和弹性。 该建议将从研究网络实体的功能以及如何将微服务体系结构映射到NFV和SDN体系结构开始。然后，它将旨在开发一种基于云的编排方法，以促进大型NFV应用程序和SDN控制器的有效设计和管理，其次，将信息技术团队的工作最小化以跟上服务水平协议的步伐以及快速变化的技术和业务模型的需求。该建议将开发ML模型，以使云部署和边缘部署之间的选择自动化，尤其是对于这些技术的关键任务应用程序（即健康意识应用程序）。这项研究将探索分析，以实现接近性检测，网络意识，资源的自我发现以及执行模式，以提高物联网系统的自给自足。它还将使用预测分析来根据预测的性能和安全需求/警报来管理资源，并根据实时流量特征动态更新策略和规则。 该研究建议将基于开源技术，并将集成团队在网络，云计算，分布式系统，ML，数据挖掘和收集，应用和数据建模，可视化技术以及管理功能方面的实用和技术专业知识。