计算机科学
可扩展性
灵活性(工程)
加速度
虚拟化
网络数据包
数据包处理
加速
试验台
嵌入式系统
分布式计算
计算机网络
操作系统
云计算
统计
物理
经典力学
数学
作者
Qiang Wu,Xiangping Zhai,Xi Liu,Chunming Wu,Fangliang Lou,Hongke Zhang
出处
期刊:IEEE ACM Transactions on Networking
[Institute of Electrical and Electronics Engineers]
日期:2023-02-01
卷期号:31 (1): 366-379
标识
DOI:10.1109/tnet.2022.3193686
摘要
Network Functions Virtualization (NFV) replaces the specialized hardware with the software-based forwarding to promise the flexibility, scalability and automation benefits. With an increasing range of applications, NFV must ultimately forward packets at rates that are comparable to the native and specialized hardware-based approaches. However, the transition packet forwarding from specialized hardware to software-based has turned out to be more challenging than expected. Thus, NFV acceleration is desperately needed to play a crucial role in the development of NFV. It is an interesting issue how to address the persistent performance tuning in a way that provides far greater flexibility to meet the demands of power. The existing developments are very inefficient, since that the uncontrollable and unanticipated performance regressions frequently occur. Besides, the environments for full system simulations are traditionally expensive and time consuming to evaluate the system performance. In this paper, we propose the methodology named as “NFV Acceleration via Lean Measurements (NALM)” to tune the performance for the NFV acceleration. NALM provides a holistic measurement approach through combining individual measures to quickly identify the bottlenecks, which can help developers with a better understanding of the design tradeoffs. Moreover, the environments for large scale performance simulation are replaced by a debugger. Thus, the waste is eliminated in terms of time consumption and infrastructure costs of the full system simulation. The systematic analysis of the multi-cores speedup ratio highlights the potential optimization space and rules. We further propose the improvement recommendations on efficient practices. The experiments evaluate the specific effects, and the relationship between the metrics and forwarding performance.
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