Intelligent Management Platform - The Future of Cloud and Networking

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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.