about 1 month ago
Senior Researcher of IP Network Technology
Research on the network architecture and key technologies guaranteed by SLA in a multi-service environment.
The advent of 5G and cloud-based network services pose higher challenges to IP networks based on packet switching best-effort forwarding, such as bearer networks, DCNs, and metro backbone networks. How to ensure E2E service experience in multi-service bearing, to build an IP network with high throughput, high reliability, no packet loss, and low latency.
IP network/mathematical modeling/AI algorithm
1. In bearer network, DCN, IP metro, and backbone scenarios, analyze the characteristics of single traffic behavior, multi-flow aggregation, and service flows in E2E mode. Perform micro-us micro-scale modeling and macro prediction and perform SLA modeling analysis on the queuing depth and latency of traffic on network devices.
2. Optimize routing protocols and innovate congestion control algorithms, traffic optimization algorithms, cache hit algorithms, and network programmability to achieve the optimal SLA combination of resources, performance, bandwidth, and latency.
3. Perform formal network verification through network modeling and high-performance verification algorithms.
4. Based on the forwarding plane and control plane, build the SLA-IP network architecture by integrating traffic modeling, network modeling, algorithm optimization, and formal verification methods.
5. Have in-depth insights into the infrastructure of the data communication domain, complete topic planning, provide technical direction and feasibility analysis, and support the industry-leading technology in the data communication domain.
1. Have a deep understanding of the datacom network architecture, traffic modeling, network topology, and network device architecture.
2. Be proficient in using mathematical formal modeling methodologies and tools, have strong mathematical modeling capabilities, have a deep understanding of application scenarios and advantages and disadvantages of various mathematical models and modeling methodologies, and be able to use methods and tools such as queuing theory and machine learning to model traffic micro features.
3. Have an in-depth understanding and insight into the macro direction and micro characteristics of traffic in video, game, and private line scenarios, and the evolution trend of service applications and transmission control mechanisms (such as BBR/Cubic/DCTCP) and the impact on traffic, have general knowledge of requirements and topologies of multiple network scenarios (DCN, Metro, IP RAN, and 5G bearer), and have experience in network traffic modeling and analysis.