Graduation Semester and Year
Summer 2024
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Department
Computer Science and Engineering
First Advisor
HUI LU
Abstract
Overlay networks are the de facto network virtualization technique for providing flexible and customized connectivity among distributed containers in the cloud. Despite their widespread adoption, overlay networks incur significant overhead due to their complexity, resulting in notable performance degradation compared to physical networks.
In this dissertation, I present our three-stage solutions aimed at addressing the challenges of efficiency and scalability in cloud-based container overlay networks: Firstly, we conduct a comprehensive empirical performance study of container overlay networks, identifying crucial parallelization bottlenecks within the kernel network stack. Our observations and root cause analysis uncover that these inefficiencies primarily arise from the increased complexity and prolonged packet processing paths introduced by additional network devices. Secondly, we propose Falcon, a fast and balanced container networking approach designed to scale the packet processing pipeline in overlay networks. Falcon pipelines software interrupts associated with different network devices of a single flow across multiple cores, thereby preventing the execution serialization of excessive software interrupts from overloading a single core. Additionally, Falcon supports multiple network flows by effectively multiplexing and balancing software interrupts among available cores. Lastly, we introduce MFLOW, a novel packet steering approach to parallelize the in-kernel data path of network flows. MFLOW exploits fine-grained packet-level parallelism by splitting packets of the same flow into multiple micro-flows, allowing parallel processing on multiple cores. This approach devises new generic mechanisms for flow splitting while preserving in-order packet delivery with minimal overhead.
Keywords
Cloud computing, Networking systems, Kernel Optimization, Datacenter Infrastructure
Disciplines
OS and Networks
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
LEI, JIAXIN, "ENHANCING THE EFFICIENCY AND SCALABILITY OF CLOUD NETWORKING SYSTEMS" (2024). Computer Science and Engineering Dissertations. 258.
https://mavmatrix.uta.edu/cse_dissertations/258